# November 28 th, Carlos Guestrin. Lower dimensional projections

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1 PCA Machine Learning 070/578 Carlos Guestrin Carnegie Mellon University November 28 th, 2007 Lower dimensional projections Rather than picking a subset of the features, we can new features that are combinations of existing features Let s see this in the unsupervised setting just X, but no Y 2

2 Linear projection and reconstruction x 2 project into -dimension z x reconstruction: only know z, what was (x,x 2 ) 3 Linear projections, a review Project a point into a (lower dimensional) space: point: x = (x,,x n ) select a basis set of basis vectors (u,,u k ) we consider orthonormal basis: u i u i =, and u i u j =0 for i j select a center defines offset of space best coordinates in lower dimensional space defined by dot-products: (z,,z k ), z i = (x-x) u i minimum squared error 4

3 PCA finds projection that minimizes reconstruction error Given m data points: x i = (x i,,x ni ), i= m Will represent each point as a projection: where: and PCA: Given k n, find (u,,u k ) minimizing reconstruction error: x 2 x 5 Understanding the reconstruction error Note that x i can be represented exactly by n-dimensional projection: Given k n, find (u,,u k ) minimizing reconstruction error: Rewriting error: 6

4 Reconstruction error and covariance matrix 7 Minimizing reconstruction error and eigen vectors Minimizing reconstruction error equivalent to picking orthonormal basis (u,,u n ) minimizing: Eigen vector: Minimizing reconstruction error equivalent to picking (u k+,,u n ) to be eigen vectors with smallest eigen values 8

5 Basic PCA algoritm Start from m by n data matrix X Recenter: subtract mean from each row of X X c X X Compute covariance matrix: Σ /m X c T X c Find eigen vectors and values of Σ Principal components: k eigen vectors with highest eigen values 9 PCA example 0

6 PCA example reconstruction only used first principal component Eigenfaces [Turk, Pentland 9] Input images: Principal components: 2

7 Eigenfaces reconstruction Each image corresponds to adding 8 principal components: 3 Scaling up Covariance matrix can be really big! Σ is n by n 0000 features! Σ finding eigenvectors is very slow Use singular value decomposition (SVD) finds to k eigenvectors great implementations available, e.g., Matlab svd 4

8 SVD Write X = W S V T X data matri one row per datapoint W weight matri one row per datapoint coordinate of x i in eigenspace S singular value matri diagonal matrix in our setting each entry is eigenvalue λ j V T singular vector matrix in our setting each row is eigenvector v j 5 PCA using SVD algoritm Start from m by n data matrix X Recenter: subtract mean from each row of X X c X X Call SVD algorithm on X c ask for k singular vectors Principal components: k singular vectors with highest singular values (rows of V T ) Coefficients become: 6

10 Markov Decision Processes (MDPs) Machine Learning 070/578 Carlos Guestrin Carnegie Mellon University November 28 th, Thus far this semester Regression: Classification: Density estimation: 20

11 Learning to act [Ng et al. 05] Reinforcement learning An agent Makes sensor observations Must select action Receives rewards positive for good states negative for bad states 2 Learning to play backgammon [Tesauro 95] Combines reinforcement learning with neural networks Played 300,000 games against itself Achieved grandmaster level! 22

12 Roadmap to learning about reinforcement learning When we learned about Bayes nets: First talked about formal framework: representation inference Then learning for BNs For reinforcement learning: Formal framework Markov decision processes Then learning 23 peasant footman building Real-time Strategy Game Peasants collect resources and build Footmen attack enemies Buildings train peasants and footmen 24

13 States and actions State space: Joint state x of entire system Action space: Joint action a= {a,, a n } for all agents 25 States change over time Like an HMM, state changes over time Next state depends on current state and action selected e.g., action= build castle likely to lead to a state where you have a castle Transition model: Dynamics of the entire system P(x 26

14 Some states and actions are better than others Each state x is associated with a reward positive reward for successful attack negative for loss Reward function: Total reward R(x) 27 Markov Decision Process (MDP) Representation State space: Joint state x of entire system Action space: Joint action a= {a,, a n } for all agents Reward function: Total reward R( sometimes reward can depend on action Transition model: Dynamics of the entire system P(x 28

15 Discounted Rewards An assistant professor gets paid, say, 20K per year. How much, in total, will the A.P. earn in their life? = Infinity \$ \$ What s wrong with this argument? 29 Discounted Rewards A reward (payment) in the future is not worth quite as much as a reward now. Because of chance of obliteration Because of inflation Example: Being promised \$0,000 next year is worth only 90% as much as receiving \$0,000 right now. Assuming payment n years in future is worth only (0.9) n of payment now, what is the AP s Future Discounted Sum of Rewards? 30

16 Discount Factors People in economics and probabilistic decision-making do this all the time. The Discounted sum of future rewards using discount factor γ is (reward now) + γ (reward in time step) + γ 2 (reward in 2 time steps) + γ 3 (reward in 3 time steps) + : : (infinite sum) 3 The Academic Life Assume Discount Factor γ = A. Assistant Prof B. Assoc. Prof T. Tenured Prof 400 Define: 0.2 S. 0.2 On the Street D. Dead 0 V A = Expected discounted future rewards starting in state A V B = Expected discounted future rewards starting in state B V T = T V S = S V D = D How do we compute V A, V B, V T, V S, V D?

17 Computing the Future Rewards of an Academic 0.6 A. Assistant Prof Assume Discount Factor γ = 0.9 B. Assoc. Prof S. On the Street D. Dead T. Tenured Prof Policy Policy: π(x) = a At state action a for all agents x 0 π(x 0 ) = both peasants get wood x π(x ) = one peasant builds barrack, other gets gold x 2 π(x 2 ) = peasants get gold, footmen attack 34

18 Value of Policy Value: V π (x) Expected longterm reward starting from x Start from x 0 x 0 R(x 0 ) π(x 0 ) x R(x ) V π (x 0 ) = E π [R(x 0 ) + γ R(x ) + γ 2 R(x 2 ) + γ 3 R(x 3 ) + γ 4 R(x 4 ) + L] π(x ) x 2 π(x 2 ) Future rewards discounted by γ 2 [0,) x R(x ) π(x ) x R(x ) π(x ) R(x 2 ) x 3 R(x 3 ) π(x 3 ) x 4 R(x 4 ) 35 Computing the value of a policy Discounted value of a state: V π (x 0 ) = E π [R(x 0 ) + γ R(x ) + γ 2 R(x 2 ) + γ 3 R(x 3 ) + γ 4 R(x 4 ) + L] value of starting from x 0 and continuing with policy π from then on A recursion! 36

19 Simple approach for computing the value of a policy: Iteratively Can solve using a simple convergent iterative approach: (a.k.a. dynamic programming) Start with some guess V 0 Iteratively say: V t+ = R + γ P π V t Stop when V t+ -V t ε means that V π -V t+ ε/(-γ) 37 But we want to learn a Policy So far, told you how good a policy is But how can we choose the best policy??? x 0 Policy: π(x) = a At state action a for all agents π(x 0 ) = both peasants get wood Suppose there was only one time step: world is about to end!!! select action that maximizes reward! x x 2 π(x ) = one peasant builds barrack, other gets gold π(x 2 ) = peasants get gold, footmen attack 38

20 Unrolling the recursion Choose actions that lead to best value in the long run Optimal value policy achieves optimal value V * 39 Bellman equation Evaluating policy π: Computing the optimal value V * - Bellman equation " V ( x) = max R( + # a! x' P( x' V " ( x' ) 40

21 Optimal Long-term Plan Optimal value function V * (x) Optimal Policy: π * (x) " # (x) = argmax a Optimal policy: R( + \$ % x' P(x' V # (x') 4 Interesting fact Unique value " V ( x) = max R( + # a! Slightly surprising fact: There is only one V * that solves Bellman equation! there may be many optimal policies that achieve V * Surprising fact: optimal policies are good everywhere!!! x' P( x' V " ( x' ) 42

22 Solve Bellman equation Solving an MDP Optimal value V * (x) " V ( x) = max R( + # a! Many algorithms solve the Bellman equations: x' P( x' V Bellman equation is non-linear!!! Optimal policy π*(x) " ( x' ) Policy iteration [Howard 60, Bellman 57] Value iteration [Bellman 57] Linear programming [Manne 60] 43 Value iteration (a.k.a. dynamic programming) the simplest of all " V ( x) = max R( + # a! x' P( x' V " ( x' ) Start with some guess V 0 Iteratively say: Stop when V t+ -V t ε means that V -V t+ ε/(-γ)! V t+ ( x) = max R( + " P( x' Vt ( x' ) a x' 44

23 A simple example You run a startup company. In every state you must choose between Saving money or Advertising. Poor & Unknown +0 S S A Rich & Unknown +0 A S Poor & Famous +0 S A Rich & Famous +0 γ = 0.9 A 45 Let s compute V t (x) for our example Poor & Unknown +0 S S A Rich & Unknown +0 A S Poor & Famous +0 S A Rich & Famous +0 γ = 0.9 A t V t (PU) V t (PF) V t (RU) V t (RF)! V t+ ( x) = max R( + " P( x' Vt ( x' ) a x' 46

24 Let s compute V t (x) for our example Poor & Unknown +0 S S A Rich & Unknown +0 A S Poor & Famous +0 S A Rich & Famous +0 γ = 0.9 A t V t (PU) V t (PF) V t (RU) V t (RF) ! V t+ ( x) = max R( + " P( x' Vt ( x' ) a x' 47 What you need to know What s a Markov decision process state, actions, transitions, rewards a policy value function for a policy computing V π Optimal value function and optimal policy Bellman equation Solving Bellman equation with value iteration, policy iteration and linear programming 48

25 Acknowledgment This lecture contains some material from Andrew Moore s excellent collection of ML tutorials: 49

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