# Nonnegative Matrix Factorization: Algorithms, Complexity and Applications

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1 Nonnegative Matrix Factorization: Algorithms, Complexity and Applications Ankur Moitra Massachusetts Institute of Technology July 6th, 2015 Ankur Moitra (MIT) NMF July 6th, 2015

2 m M n

3 W m M = A inner dimension n

4 rank W m M = A inner dimension n

5 rank non negative W m M = A inner dimension n non negative

6 rank non negative non negative W m M = A inner dimension n non negative

7 Equivalent Definitions The nonnegative rank rank + (M) can be defined many ways:

8 Equivalent Definitions The nonnegative rank rank + (M) can be defined many ways: The smallest r such that there is a factorization M = AW where A and W are nonnegative and have inner-dimension r

9 Equivalent Definitions The nonnegative rank rank + (M) can be defined many ways: The smallest r such that there is a factorization M = AW where A and W are nonnegative and have inner-dimension r The smallest r such that there are r nonnegative vectors A 1, A 2,...A r and every column in M can be represented as a nonnegative combination of them

10 Equivalent Definitions The nonnegative rank rank + (M) can be defined many ways: The smallest r such that there is a factorization M = AW where A and W are nonnegative and have inner-dimension r The smallest r such that there are r nonnegative vectors A 1, A 2,...A r and every column in M can be represented as a nonnegative combination of them The smallest r such that M = r i=1 M (i) where each M (i) is rank one and nonnegative

11 Equivalent Definitions The nonnegative rank rank + (M) can be defined many ways: The smallest r such that there is a factorization M = AW where A and W are nonnegative and have inner-dimension r The smallest r such that there are r nonnegative vectors A 1, A 2,...A r and every column in M can be represented as a nonnegative combination of them The smallest r such that M = r i=1 M (i) where each M (i) is rank one and nonnegative rank(m) rank + (M), but it can be much larger too!

12 Information Retrieval documents:

13 Information Retrieval documents: m M n

14 Information Retrieval documents: W m M = A inner dimension n

15 Information Retrieval documents: "topics" W m M = A inner dimension n

16 Information Retrieval documents: "topics" "representation" W m M = A inner dimension n

17 Information Retrieval documents: "topics" "representation" W m M = A inner dimension n

18 Information Retrieval documents: "topics" "representation" W m M = A inner dimension n

19 Applications Statistics and Machine Learning: extract latent relationships in data image segmentation, text classification, information retrieval, collaborative filtering,... [Lee, Seung], [Xu et al], [Hofmann], [Kumar et al], [Kleinberg, Sandler]

20 Applications Statistics and Machine Learning: extract latent relationships in data image segmentation, text classification, information retrieval, collaborative filtering,... [Lee, Seung], [Xu et al], [Hofmann], [Kumar et al], [Kleinberg, Sandler] Combinatorics: extended formulation, log-rank conjecture [Yannakakis], [Lovász, Saks]

21 Applications Statistics and Machine Learning: extract latent relationships in data image segmentation, text classification, information retrieval, collaborative filtering,... [Lee, Seung], [Xu et al], [Hofmann], [Kumar et al], [Kleinberg, Sandler] Combinatorics: extended formulation, log-rank conjecture [Yannakakis], [Lovász, Saks] Physical Modeling: interaction of components is additive visual recognition, environmetrics

22 Local Search: Given A, compute W, compute A,...

23 Local Search: Given A, compute W, compute A,... Known to fail on worst-case inputs (stuck in local minima)

24 Local Search: Given A, compute W, compute A,... Known to fail on worst-case inputs (stuck in local minima) Highly sensitive to cost function, regularization, update procedure

25 Local Search: Given A, compute W, compute A,... Known to fail on worst-case inputs (stuck in local minima) Highly sensitive to cost function, regularization, update procedure Question Are there efficient provable algorithms to compute the nonnegative rank?

26 Local Search: Given A, compute W, compute A,... Known to fail on worst-case inputs (stuck in local minima) Highly sensitive to cost function, regularization, update procedure Question Are there efficient provable algorithms to compute the nonnegative rank? Can it even be computed in finite time?

27 Local Search: Given A, compute W, compute A,... Known to fail on worst-case inputs (stuck in local minima) Highly sensitive to cost function, regularization, update procedure Question Are there efficient provable algorithms to compute the nonnegative rank? Can it even be computed in finite time? Theorem (Cohen, Rothblum) There is an exact algorithm (based on solving systems of polynomial inequalities) that runs in time exponential in n, m and r

28 Hardness of NMF Theorem (Vavasis) NMF is NP-hard to compute

29 Hardness of NMF Theorem (Vavasis) NMF is NP-hard to compute Hence it is unlikely that there is an exact algorithm that runs in time polynomial in n, m and r

30 Hardness of NMF Theorem (Vavasis) NMF is NP-hard to compute Hence it is unlikely that there is an exact algorithm that runs in time polynomial in n, m and r Question Should we expect r to be large?

31 Hardness of NMF Theorem (Vavasis) NMF is NP-hard to compute Hence it is unlikely that there is an exact algorithm that runs in time polynomial in n, m and r Question Should we expect r to be large? What if you gave me a collection of 100 documents, and I told you there are 75 topics?

32 Hardness of NMF Theorem (Vavasis) NMF is NP-hard to compute Hence it is unlikely that there is an exact algorithm that runs in time polynomial in n, m and r Question Should we expect r to be large? What if you gave me a collection of 100 documents, and I told you there are 75 topics? How quickly can we solve NMF if r is small?

33 The Worst-Case Complexity of NMF Theorem (Arora, Ge, Kannan, Moitra) There is an (nm) O(2r r 2) time exact algorithm for NMF

34 The Worst-Case Complexity of NMF Theorem (Arora, Ge, Kannan, Moitra) There is an (nm) O(2r r 2) time exact algorithm for NMF Previously, the fastest (provable) algorithm for r = 4 ran in time exponential in n and m

35 The Worst-Case Complexity of NMF Theorem (Arora, Ge, Kannan, Moitra) There is an (nm) O(2r r 2) time exact algorithm for NMF Previously, the fastest (provable) algorithm for r = 4 ran in time exponential in n and m Can we improve the exponential dependence on r?

36 The Worst-Case Complexity of NMF Theorem (Arora, Ge, Kannan, Moitra) There is an (nm) O(2r r 2) time exact algorithm for NMF Previously, the fastest (provable) algorithm for r = 4 ran in time exponential in n and m Can we improve the exponential dependence on r? Theorem (Arora, Ge, Kannan, Moitra) An exact algorithm for NMF that runs in time (nm) o(r) would yield a sub-exponential time algorithm for 3-SAT

37 An Almost Optimal Algorithm Theorem (Moitra) There is an (2 r nm) O(r 2) time exact algorithm for NMF

38 An Almost Optimal Algorithm Theorem (Moitra) There is an (2 r nm) O(r 2) time exact algorithm for NMF These algorithms are based on methods for variable reduction

39 An Almost Optimal Algorithm Theorem (Moitra) There is an (2 r nm) O(r 2) time exact algorithm for NMF These algorithms are based on methods for variable reduction Question How many variables are needed to express a decision problem as a feasibility problem for a system of polynomial inequalities?

40 An Almost Optimal Algorithm Theorem (Moitra) There is an (2 r nm) O(r 2) time exact algorithm for NMF These algorithms are based on methods for variable reduction Question How many variables are needed to express a decision problem as a feasibility problem for a system of polynomial inequalities? Open Question Is the true complexity of NMF exponential in r 2 or r?

41 Outline Introduction Algebraic Algorithms for NMF Systems of Polynomial Inequalities Methods for Variable Reduction Hardness Results Intermediate Simplex Problem Geometric Gadgets Separable NMF The Anchor Words Algorithm Applications to Topic Modeling

42 Outline Introduction Algebraic Algorithms for NMF Systems of Polynomial Inequalities Methods for Variable Reduction Hardness Results Intermediate Simplex Problem Geometric Gadgets Separable NMF The Anchor Words Algorithm Applications to Topic Modeling

43 Is NMF Computable?

44 Is NMF Computable? [Cohen and Rothblum]: Yes

45 Is NMF Computable? [Cohen and Rothblum]: Yes (DETOUR)

46 Semi-algebraic sets: s polynomials, k variables, Boolean function B S = {x 1, x 2...x k B(sgn(f 1 ), sgn(f 2 ),...sgn(f s )) = true }

47 Semi-algebraic sets: s polynomials, k variables, Boolean function B Question S = {x 1, x 2...x k B(sgn(f 1 ), sgn(f 2 ),...sgn(f s )) = true } How many sign patterns arise (as x 1, x 2,...x k range over R k )?

48 Semi-algebraic sets: s polynomials, k variables, Boolean function B Question S = {x 1, x 2...x k B(sgn(f 1 ), sgn(f 2 ),...sgn(f s )) = true } How many sign patterns arise (as x 1, x 2,...x k range over R k )? Naive bound: 3 s (all of { 1, 0, 1} s ),

49 Semi-algebraic sets: s polynomials, k variables, Boolean function B Question S = {x 1, x 2...x k B(sgn(f 1 ), sgn(f 2 ),...sgn(f s )) = true } How many sign patterns arise (as x 1, x 2,...x k range over R k )? Naive bound: 3 s (all of { 1, 0, 1} s ), [Milnor, Warren]: at most (ds) k, where d is the maximum degree

50 Semi-algebraic sets: s polynomials, k variables, Boolean function B Question S = {x 1, x 2...x k B(sgn(f 1 ), sgn(f 2 ),...sgn(f s )) = true } How many sign patterns arise (as x 1, x 2,...x k range over R k )? Naive bound: 3 s (all of { 1, 0, 1} s ), [Milnor, Warren]: at most (ds) k, where d is the maximum degree In fact, best known algorithms (e.g. [Renegar]) for finding a point in S run in (ds) O(k) time

51 Is NMF Computable? [Cohen, Rothblum]: Yes (DETOUR)

52 Is NMF Computable? [Cohen, Rothblum]: Yes (DETOUR) Variables: entries in A and W (nr + mr total)

53 Is NMF Computable? [Cohen, Rothblum]: Yes (DETOUR) Variables: entries in A and W (nr + mr total) Constraints: A, W 0 and AW = M (degree two)

54 Is NMF Computable? [Cohen, Rothblum]: Yes (DETOUR) Variables: entries in A and W (nr + mr total) Constraints: A, W 0 and AW = M (degree two) Running time for a solver is exponential in the number of variables

55 Is NMF Computable? [Cohen, Rothblum]: Yes (DETOUR) Variables: entries in A and W (nr + mr total) Constraints: A, W 0 and AW = M (degree two) Running time for a solver is exponential in the number of variables Question What is the smallest formulation, measured in the number of variables?

56 Is NMF Computable? [Cohen, Rothblum]: Yes (DETOUR) Variables: entries in A and W (nr + mr total) Constraints: A, W 0 and AW = M (degree two) Running time for a solver is exponential in the number of variables Question What is the smallest formulation, measured in the number of variables? Can we use only f (r) variables?

57 Is NMF Computable? [Cohen, Rothblum]: Yes (DETOUR) Variables: entries in A and W (nr + mr total) Constraints: A, W 0 and AW = M (degree two) Running time for a solver is exponential in the number of variables Question What is the smallest formulation, measured in the number of variables? Can we use only f (r) variables? O(r 2 ) variables?

58 Outline Introduction Algebraic Algorithms for NMF Systems of Polynomial Inequalities Methods for Variable Reduction Hardness Results Intermediate Simplex Problem Geometric Gadgets Separable NMF The Anchor Words Algorithm Applications to Topic Modeling

59 Outline Introduction Algebraic Algorithms for NMF Systems of Polynomial Inequalities Methods for Variable Reduction Hardness Results Intermediate Simplex Problem Geometric Gadgets Separable NMF The Anchor Words Algorithm Applications to Topic Modeling

60 Easy Case: A has Full Column Rank (AGKM) A

61 Easy Case: A has Full Column Rank (AGKM) A + pseudo inverse A

62 Easy Case: A has Full Column Rank (AGKM) A + pseudo inverse A = I r

63 Easy Case: A has Full Column Rank (AGKM) A + pseudo inverse W A = W

64 Easy Case: A has Full Column Rank (AGKM) A + pseudo inverse W A = M W

65 Easy Case: A has Full Column Rank (AGKM) linearly independent A + pseudo inverse W A = M W

66 Easy Case: A has Full Column Rank (AGKM) linearly independent A + pseudo inverse W A = M W change of basis T M

67 Putting it Together: Easy Case M

68 Putting it Together: Easy Case M M M

69 Putting it Together: Easy Case T M M variables S M

70 Putting it Together: Easy Case T M M variables W S M A

71 Putting it Together: Easy Case T M M variables W non negative? S M A non negative?

72 Putting it Together: Easy Case T M M variables W non negative? S M A ( ) ( ) non negative?

73 Putting it Together: Easy Case T M M variables W non negative? M S A? ( ) ( ) = M non negative?

74 General Structure Theorem Question What if A does not have full column rank?

75 General Structure Theorem Question What if A does not have full column rank? Approach: guess many linear transformations, one for each pseudo-inverse of a set of linearly independent columns

76 A 2 A 1 A 4 A 3 M

77 A 2 A 1 A 4 A 3 M

78 A 2 A 1 A 4 A 3 M T 1 = ( 2 ) + A 1 A A 3

79 A 2 A 1 A 4 A 3 M T 1 T 2 = ( ) + A 1 A 2 A 3 = ( + A2A 3 A 4)

80 A 2 A 1 A 4 A 3 M T 1 T 2 = ( A 2 A 3 ) + A 1 = ( ) + A 2 A 3 A 4

81 A 2 A 1 A 4 A 3 M T 1 T 2 = ( A 2 A 3 ) + A 1 = ( ) + A 2 A 3 A 4

82 A 2 A 1 A 4 A 3 M T 1 T 2 = ( A 2 A 3 ) + A 1 = ( ) + A 2 A 3 A 4

83 A 2 A 1 A 4 A 3 M T 1 T 2 = ( A 2 A 3 ) + A 1 = ( ) + A 2 A 3 A 4

84 A 2 A 1 A 4 A 3 M T 1 T 2 = ( A 2 A 3 ) + A 1 = ( ) + A 2 A 3 A 4

85 A 2 A 1 A 4 A 3 M exponentially many choices? T 1 T 2 = ( A 2 A 3 ) + A 1 = ( ) + A 2 A 3 A 4

86 General Structure Theorem Question What if A does not have full column rank? Approach: guess many linear transformations, one for each pseudo-inverse of a set of linearly independent columns

87 General Structure Theorem Question What if A does not have full column rank? Approach: guess many linear transformations, one for each pseudo-inverse of a set of linearly independent columns Problem Which linear transformation should we use (for a column of M)?

88 A 2 A 1 A 4 A 3 M exponentially many choices? T 1 T 2 = ( A 2 A 3 ) + A 1 = ( ) + A 2 A 3 A 4

89 A 2 A 1 A 4 A 3 M exponentially many choices? T 1 T 2 = ( A 2 3) + A 1 A = ( ) + A 2 A 3 A 4

90 A 2 A 1 A 4 A 3 M exponentially many choices? T 1 T 2 = ( A 2 3) + A 1 A = ( ) + A 2 A 3 A 4

91 A 2 A 1 A 4 A 3 M exponentially many choices? T 1 T 2 = ( A 1 A 2 A 3 ) + = ( ) + A 2 A 3 A 4

92 A 2 A 1 A 4 A 3 M exponentially many choices? T 1 T 2 = ( A 1 A 2 A 3 ) + = ( ) + A 2 A 3 A 4

93 A 2 A 1 A 4 A 3 M exponentially many choices? T 1 T 2 = ( A 1 A 2 A 3 ) + = ( ) + A 2 A 3 A 4

94 A 2 A 1 A 4 A 3 M exponentially many choices? T 1 T 2 = ( A 1 A 2 A 3 ) + = ( ) + A 2 A 3 A 4 polynomially many choices

95 General Structure Theorem Question What if A does not have full column rank? Approach: guess many linear transformations, one for each pseudo-inverse of a set of linearly independent columns Problem Which linear transformation should we use (for a column of M)?

96 General Structure Theorem Question What if A does not have full column rank? Approach: guess many linear transformations, one for each pseudo-inverse of a set of linearly independent columns Problem Which linear transformation should we use (for a column of M)? Observation Which linear transformation depends on a partition of space defined by at most 2 r r 2 half spaces in r dimensional space

97 Putting it Together: The General Case We can brute force search over all such decision rules, and solve a system of polynomial equations for each

98 Putting it Together: The General Case We can brute force search over all such decision rules, and solve a system of polynomial equations for each Claim (Soundness) If any resulting system has a solution, it yields a nonnegative matrix factorization

99 Putting it Together: The General Case We can brute force search over all such decision rules, and solve a system of polynomial equations for each Claim (Soundness) If any resulting system has a solution, it yields a nonnegative matrix factorization Claim (Completeness) If there is a nonnegative matrix factorization, at least one of the systems will have a solution

100 Algebraic Interpretation Theorem (Arora, Ge, Kannan, Moitra) Given a nonnegative matrix M and a target r there is a one-to-many reduction to a set of (nm) 2r r 2 systems of polynomial inequalities each in 2 r r 2 variables

101 Algebraic Interpretation Theorem (Arora, Ge, Kannan, Moitra) Given a nonnegative matrix M and a target r there is a one-to-many reduction to a set of (nm) 2r r 2 systems of polynomial inequalities each in 2 r r 2 variables This reduction is (essentially) based on covering the convex hull of the columns of A by 2 r simplices

102 Algebraic Interpretation Theorem (Arora, Ge, Kannan, Moitra) Given a nonnegative matrix M and a target r there is a one-to-many reduction to a set of (nm) 2r r 2 systems of polynomial inequalities each in 2 r r 2 variables This reduction is (essentially) based on covering the convex hull of the columns of A by 2 r simplices Is there a more efficient reduction that uses fewer variables?

103 Algebraic Interpretation Theorem (Arora, Ge, Kannan, Moitra) Given a nonnegative matrix M and a target r there is a one-to-many reduction to a set of (nm) 2r r 2 systems of polynomial inequalities each in 2 r r 2 variables This reduction is (essentially) based on covering the convex hull of the columns of A by 2 r simplices Is there a more efficient reduction that uses fewer variables? Problem If A does not have full column rank, we may need as many as 2r r+1 simplices to cover it (and only it)

104 The Cross Polytope Let K r = {x s.t. r i=1 x i 1} = conv{±e i } i.e.

105 The Cross Polytope Let K r = {x s.t. r i=1 x i 1} = conv{±e i } i.e. Fact K r has 2r vertices and 2 r facets (it is dual to the hypercube)

106 Question If we want to cover K r with simplices (but cover nothing else) how many simplices do we need?

107 Question If we want to cover K r with simplices (but cover nothing else) how many simplices do we need? 2 Each simplex covers at most r + 1 facets, so we need at least r simples r+1

108 Question If we want to cover K r with simplices (but cover nothing else) how many simplices do we need? 2 Each simplex covers at most r + 1 facets, so we need at least r simples In our setting, this means we really do need exponentially many linear transformations to recover the factorization from our input r+1

109 Question If we want to cover K r with simplices (but cover nothing else) how many simplices do we need? 2 Each simplex covers at most r + 1 facets, so we need at least r simples In our setting, this means we really do need exponentially many linear transformations to recover the factorization from our input However these linear transformations are algebraically dependent! r+1

110 Fact (Cramer s Rule) If R is invertible, the entries of R 1 are ratios of polynomials of entries in R

111 Fact (Cramer s Rule) If R is invertible, the entries of R 1 are ratios of polynomials of entries in R More precisely, (R 1 ) i,j = det(r i,j ) det(r)

112 Fact (Cramer s Rule) If R is invertible, the entries of R 1 are ratios of polynomials of entries in R More precisely, (R 1 ) i,j = det(r i,j ) det(r) Claim We can express the entries of the 2 r unknown linear transformations as ratios of polynomials in the unknown entries of any invertible submatrix of A and W.

113 Fact (Cramer s Rule) If R is invertible, the entries of R 1 are ratios of polynomials of entries in R More precisely, (R 1 ) i,j = det(r i,j ) det(r) Claim We can express the entries of the 2 r unknown linear transformations as ratios of polynomials in the unknown entries of any invertible submatrix of A and W. Thus we only need 2r 2 variables to express the unknown linear transformations

114 Summary of Variable Reduction How many variables do we need to express the decision problem rank + (M) r?

115 Summary of Variable Reduction How many variables do we need to express the decision problem rank + (M) r? [Cohen, Rothblum]: mr + nr

116 Summary of Variable Reduction How many variables do we need to express the decision problem rank + (M) r? [Cohen, Rothblum]: mr + nr [Arora, Ge, Kannan, Moitra]: 2r 2 2 r

117 Summary of Variable Reduction How many variables do we need to express the decision problem rank + (M) r? [Cohen, Rothblum]: mr + nr [Arora, Ge, Kannan, Moitra]: 2r 2 2 r [Moitra]: 2r 2

118 Summary of Variable Reduction How many variables do we need to express the decision problem rank + (M) r? [Cohen, Rothblum]: mr + nr [Arora, Ge, Kannan, Moitra]: 2r 2 2 r [Moitra]: 2r 2 Corollary There is an (2 r nm) O(r 2) time algorithm for NMF

119 Summary of Variable Reduction How many variables do we need to express the decision problem rank + (M) r? [Cohen, Rothblum]: mr + nr [Arora, Ge, Kannan, Moitra]: 2r 2 2 r [Moitra]: 2r 2 Corollary There is an (2 r nm) O(r 2) time algorithm for NMF Are there other applications of variable reduction in geometric problems?

120 Outline Introduction Algebraic Algorithms for NMF Systems of Polynomial Inequalities Methods for Variable Reduction Hardness Results Intermediate Simplex Problem Geometric Gadgets Separable NMF The Anchor Words Algorithm Applications to Topic Modeling

121 Outline Introduction Algebraic Algorithms for NMF Systems of Polynomial Inequalities Methods for Variable Reduction Hardness Results Intermediate Simplex Problem Geometric Gadgets Separable NMF The Anchor Words Algorithm Applications to Topic Modeling

122 Other Algorithms? Question Are there other algorithms (perhaps non-algebraic) that can solve NMF much faster?

123 Other Algorithms? Question Are there other algorithms (perhaps non-algebraic) that can solve NMF much faster? Probably not, under standard complexity assumptions

124 Other Algorithms? Question Are there other algorithms (perhaps non-algebraic) that can solve NMF much faster? Probably not, under standard complexity assumptions Theorem (Vavasis) NMF is NP-hard to compute

125 Other Algorithms? Question Are there other algorithms (perhaps non-algebraic) that can solve NMF much faster? Probably not, under standard complexity assumptions Theorem (Vavasis) NMF is NP-hard to compute Open Question Is NMF NP-hard when restricted to Boolean matrices?

126 Intermediate Simplex Problem:

127 Intermediate Simplex Problem: Input: polytopes P Q where P is encoded by its vertices and Q is encoded by its facets.

128 Intermediate Simplex Problem: Input: polytopes P Q where P is encoded by its vertices and Q is encoded by its facets. Is there a simplex K such that P K Q?

129 Intermediate Simplex Problem: Input: polytopes P Q where P is encoded by its vertices and Q is encoded by its facets. Is there a simplex K such that P K Q? Theorem (Vavasis) The intermediate simplex problem and a special case of nonnegative matrix factorization are polynomial time inter-reducible

130 Intermediate Simplex Problem: Input: polytopes P Q where P is encoded by its vertices and Q is encoded by its facets. Is there a simplex K such that P K Q? Theorem (Vavasis) The intermediate simplex problem and a special case of nonnegative matrix factorization are polynomial time inter-reducible How can we construct geometric gadgets that encode SAT?

131 Outline Introduction Algebraic Algorithms for NMF Systems of Polynomial Inequalities Methods for Variable Reduction Hardness Results Intermediate Simplex Problem Geometric Gadgets Separable NMF The Anchor Words Algorithm Applications to Topic Modeling

132 Outline Introduction Algebraic Algorithms for NMF Systems of Polynomial Inequalities Methods for Variable Reduction Hardness Results Intermediate Simplex Problem Geometric Gadgets Separable NMF The Anchor Words Algorithm Applications to Topic Modeling

133 Vavasis:

134 Vavasis: place three points!

135 Each gadget represents a variable and forces it to be true or false

136 Each gadget represents a variable and forces it to be true or false But this approach requires r to be large (at least the number of variables in a SAT formula).

137 Each gadget represents a variable and forces it to be true or false But this approach requires r to be large (at least the number of variables in a SAT formula). Question Are there lower dimensional gadgets that can be used to encode hard problems?

138 Each gadget represents a variable and forces it to be true or false But this approach requires r to be large (at least the number of variables in a SAT formula). Question Are there lower dimensional gadgets that can be used to encode hard problems? d-sum Problem:

139 Each gadget represents a variable and forces it to be true or false But this approach requires r to be large (at least the number of variables in a SAT formula). Question Are there lower dimensional gadgets that can be used to encode hard problems? d-sum Problem: Input: n numbers s 1, s 2,...s n R

140 Each gadget represents a variable and forces it to be true or false But this approach requires r to be large (at least the number of variables in a SAT formula). Question Are there lower dimensional gadgets that can be used to encode hard problems? d-sum Problem: Input: n numbers s 1, s 2,...s n R Is there a set of d distinct numbers U [n] where i U s i = 0?

141 Hardness for d-sum The best known algorithms for d-sum run in time n d/2

142 Hardness for d-sum The best known algorithms for d-sum run in time n d/2 Theorem (Patrascu, Williams) If d-sum can be solved in time n o(d) then there are subexponential time algorithms for 3-SAT.

143 Hardness for d-sum The best known algorithms for d-sum run in time n d/2 Theorem (Patrascu, Williams) If d-sum can be solved in time n o(d) then there are subexponential time algorithms for 3-SAT. Conjecture (Impagliazzo, Paturi) 3-SAT on n variables cannot be solved in time 2 o(n)

144 Hardness for d-sum The best known algorithms for d-sum run in time n d/2 Theorem (Patrascu, Williams) If d-sum can be solved in time n o(d) then there are subexponential time algorithms for 3-SAT. Conjecture (Impagliazzo, Paturi) 3-SAT on n variables cannot be solved in time 2 o(n) Can we use d-sum as our source of hardness?

145 Vavasis: place three points!

146 Vavasis: d SUM gadget: place three points!

147 Vavasis: d SUM gadget: eps place three points!

148 Vavasis: d SUM gadget: eps place three points!

149 Vavasis: d SUM gadget: eps s 1 s 2 s 3 place three points!

150 Vavasis: d SUM gadget: eps s 1 s 2 s 3 place three points!

151 Summary of Hardness Results Theorem (Vavasis) NMF is NP-hard

152 Summary of Hardness Results Theorem (Vavasis) NMF is NP-hard Theorem (Arora, Ge, Kannan, Moitra) An algorithm for NMF that runs in time (nm) o(r) would yield a sub-exponential time algorithm for 3-SAT

153 Summary of Hardness Results Theorem (Vavasis) NMF is NP-hard Theorem (Arora, Ge, Kannan, Moitra) An algorithm for NMF that runs in time (nm) o(r) would yield a sub-exponential time algorithm for 3-SAT Open Question Is it hard to solve NMF in time (nm) o(r 2)?

154 Beyond Worst-Case Analysis We (essentially) resolved the worst-case complexity of NMF, but there are still paths to obtaining better algorithms

155 Beyond Worst-Case Analysis We (essentially) resolved the worst-case complexity of NMF, but there are still paths to obtaining better algorithms Question What distinguishes a realistic instance of NMF from an artificial one?

156 Beyond Worst-Case Analysis We (essentially) resolved the worst-case complexity of NMF, but there are still paths to obtaining better algorithms Question What distinguishes a realistic instance of NMF from an artificial one? Question Are there practical algorithms with provable guarantees?

157 Beyond Worst-Case Analysis We (essentially) resolved the worst-case complexity of NMF, but there are still paths to obtaining better algorithms Question What distinguishes a realistic instance of NMF from an artificial one? Question Are there practical algorithms with provable guarantees? Recall, in some applications columns in A represent distributions on words (topics) and columns in W represent distributions on topics

158 [Donoho, Stodden]: A is separable if each column i has an unknown row π(i) whose only non-zero is in column i

159 A

160 A

161 [Donoho, Stodden]: A is separable if each column i has an unknown row π(i) whose only non-zero is in column i

162 [Donoho, Stodden]: A is separable if each column i has an unknown row π(i) whose only non-zero is in column i Interpretation: We call this row an anchor word, and any document that contains this word is very likely to be (at least partially) about the corresponding topic

163 [Donoho, Stodden]: A is separable if each column i has an unknown row π(i) whose only non-zero is in column i Interpretation: We call this row an anchor word, and any document that contains this word is very likely to be (at least partially) about the corresponding topic e.g. personal finance 401k, baseball bunt,...

164 [Donoho, Stodden]: A is separable if each column i has an unknown row π(i) whose only non-zero is in column i Interpretation: We call this row an anchor word, and any document that contains this word is very likely to be (at least partially) about the corresponding topic e.g. personal finance 401k, baseball bunt,... Question Separability was introduced to understand when NMF is unique Is it enough to make NMF easy?

165 A W M =

166 A W M =

167 Separable Instances Recall: For each topic, there is some (anchor) word that only appears in this topic

168 Separable Instances Recall: For each topic, there is some (anchor) word that only appears in this topic Observation Rows of W appear as (scaled) rows of M

169 Separable Instances Recall: For each topic, there is some (anchor) word that only appears in this topic Observation Rows of W appear as (scaled) rows of M Question How can we identify anchor words?

170 A W M =

171 A W M = brute force: n r

172 A W M = brute force: n r

173 A W M = brute force: n r

174 A W M = brute force: n r

175 A W M = brute force: n r

176 A W M = brute force: n r

177 Separable Instances Recall: For each topic, there is some (anchor) word that only appears in this topic Observation Rows of W appear as (scaled) rows of M Question How can we identify anchor words?

178 Separable Instances Recall: For each topic, there is some (anchor) word that only appears in this topic Observation Rows of W appear as (scaled) rows of M Question How can we identify anchor words? Removing a row from M strictly changes the convex hull iff it is an anchor word

179 Separable Instances Recall: For each topic, there is some (anchor) word that only appears in this topic Observation Rows of W appear as (scaled) rows of M Question How can we identify anchor words? Removing a row from M strictly changes the convex hull iff it is an anchor word Hence we can identify all the anchor words via linear programming

180 Separable Instances Recall: For each topic, there is some (anchor) word that only appears in this topic Observation Rows of W appear as (scaled) rows of M Question How can we identify anchor words? Removing a row from M strictly changes the convex hull iff it is an anchor word Hence we can identify all the anchor words via linear programming (can be made robust to noise)

181 Outline Introduction Algebraic Algorithms for NMF Systems of Polynomial Inequalities Methods for Variable Reduction Hardness Results Intermediate Simplex Problem Geometric Gadgets Separable NMF The Anchor Words Algorithm Applications to Topic Modeling

182 Outline Introduction Algebraic Algorithms for NMF Systems of Polynomial Inequalities Methods for Variable Reduction Hardness Results Intermediate Simplex Problem Geometric Gadgets Separable NMF The Anchor Words Algorithm Applications to Topic Modeling

183 Topic Models Topic matrix A, generate W stochastically

184 Topic Models Topic matrix A, generate W stochastically For each document (column in M = AW ) sample N words

185 Topic Models Topic matrix A, generate W stochastically For each document (column in M = AW ) sample N words e.g. Latent Dirichlet Allocation (LDA), Correlated Topic Model (CTM), Pachinko Allocation Model (PAM),...

186 Topic Models Topic matrix A, generate W stochastically For each document (column in M = AW ) sample N words e.g. Latent Dirichlet Allocation (LDA), Correlated Topic Model (CTM), Pachinko Allocation Model (PAM),... Question Can we estimate A, given random samples from M?

187 Topic Models Topic matrix A, generate W stochastically For each document (column in M = AW ) sample N words e.g. Latent Dirichlet Allocation (LDA), Correlated Topic Model (CTM), Pachinko Allocation Model (PAM),... Question Can we estimate A, given random samples from M? Yes! [Arora, Ge, Moitra] we give a provable algorithm based on (noise-tolerant) NMF;

188 Topic Models Topic matrix A, generate W stochastically For each document (column in M = AW ) sample N words e.g. Latent Dirichlet Allocation (LDA), Correlated Topic Model (CTM), Pachinko Allocation Model (PAM),... Question Can we estimate A, given random samples from M? Yes! [Arora, Ge, Moitra] we give a provable algorithm based on (noise-tolerant) NMF; see also [Anandkumar et al]

189 Topic Models Topic matrix A, generate W stochastically For each document (column in M = AW ) sample N words e.g. Latent Dirichlet Allocation (LDA), Correlated Topic Model (CTM), Pachinko Allocation Model (PAM),... Question Can we estimate A, given random samples from M? Yes! [Arora, Ge, Moitra] we give a provable algorithm based on (noise-tolerant) NMF; see also [Anandkumar et al] Danger: I am about to show you experimental results

190 Running on Real Data joint work with Arora, Ge, Halpern, Mimno, Sontag, Wu and Zhu We ran our algorithm on a database of 300,000 New York Times articles (from the UCI database) with 30,000 distinct words

191 Running on Real Data joint work with Arora, Ge, Halpern, Mimno, Sontag, Wu and Zhu We ran our algorithm on a database of 300,000 New York Times articles (from the UCI database) with 30,000 distinct words Run time: 12 minutes

192 Running on Real Data joint work with Arora, Ge, Halpern, Mimno, Sontag, Wu and Zhu We ran our algorithm on a database of 300,000 New York Times articles (from the UCI database) with 30,000 distinct words Run time: 12 minutes (compared to 10 hours for MALLET and other state-of-the-art topic modeling tools)

193 Running on Real Data joint work with Arora, Ge, Halpern, Mimno, Sontag, Wu and Zhu We ran our algorithm on a database of 300,000 New York Times articles (from the UCI database) with 30,000 distinct words Run time: 12 minutes (compared to 10 hours for MALLET and other state-of-the-art topic modeling tools) Topics are high quality

194 Running on Real Data joint work with Arora, Ge, Halpern, Mimno, Sontag, Wu and Zhu We ran our algorithm on a database of 300,000 New York Times articles (from the UCI database) with 30,000 distinct words Run time: 12 minutes (compared to 10 hours for MALLET and other state-of-the-art topic modeling tools) Topics are high quality (because we can handle correlations in the topic model?)

195 Running on Real Data joint work with Arora, Ge, Halpern, Mimno, Sontag, Wu and Zhu We ran our algorithm on a database of 300,000 New York Times articles (from the UCI database) with 30,000 distinct words Run time: 12 minutes (compared to 10 hours for MALLET and other state-of-the-art topic modeling tools) Topics are high quality (because we can handle correlations in the topic model?) Let s check out the results!

196 Concluding Remarks Our algorithms are based on answering a purely algebraic question: How many variables do we need in a semi-algebraic set to encode nonnegative rank?

197 Concluding Remarks Our algorithms are based on answering a purely algebraic question: How many variables do we need in a semi-algebraic set to encode nonnegative rank? Question Are there other examples of a better understanding of the expressive power of semi-algebraic sets can lead to new algorithms?

198 Concluding Remarks Our algorithms are based on answering a purely algebraic question: How many variables do we need in a semi-algebraic set to encode nonnegative rank? Question Are there other examples of a better understanding of the expressive power of semi-algebraic sets can lead to new algorithms? Observation The number of variables plays an analogous role to VC-dimension

199 Concluding Remarks Our algorithms are based on answering a purely algebraic question: How many variables do we need in a semi-algebraic set to encode nonnegative rank? Question Are there other examples of a better understanding of the expressive power of semi-algebraic sets can lead to new algorithms? Observation The number of variables plays an analogous role to VC-dimension Is there an elementary proof of the Milnor-Warren bound?

200 Any Questions?

201 Thanks!

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