# A Survey of Kernel Clustering Methods

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1 A Survey of Kernel Clustering Methods Maurizio Filippone, Francesco Camastra, Francesco Masulli and Stefano Rovetta Presented by: Kedar Grama

2 Outline Unsupervised Learning and Clustering Types of clustering algorithms Clustering Algorithms Partitioning Methods (in Euclidean Space) K-Means Self Organizing Maps(SOM) Neural Gas Fuzzy C-Means Probabilistic C-Means Kernel Clustering Methods Kernel K-Means Kernel SOM Kernel Neural Gas Kernel Fuzzy C-Means Kernel Probabilistic C-Means One class SVMs and Support Vector Clustering

3 Unsupervised Learning And Clustering Supervised learning - human effort involved Example: Learning conditional distribution P(Y X), X: features, Y: classes Unsupervised learning - no human effort involved Example: learning distribution P(X), X: features Definition: Clustering is the task of grouping a set of objects in such that objects in the same group are more similar to each other than to those in other groups

4 Types of Clustering Algorithms Clustering Algorithms Flat Algorithms Hierarchical Algorithms Hard Partitioning Soft Partitioning Examples: K-Means Self Organizing maps DBSCAN Single Linkage Examples: Expectation Maximization Fuzzy Clustering Methods Complete Linkage Other Linkages

5 K-means Objective: Minimize the empirical quantization error E(X) Algorithm: 1. choose the number k of clusters; 2. initialize the codebook V with vectors randomly picked from X; 3. compute the Voronoi set i associated to the code vector v i ; 4. move each code vector to the mean of its Voronoi set 5. return to step 3 if any code vector has changed otherwise 6. return the codebook.

6 K-means Vizualization Initalize Partition Update Output Redo partition if update is not small Source:

7 Kernel Clustering Basics Mercer Kernels: Polynomial: Gaussian: Distances in kernel space can be computed by using the distance kernel trick First map the data set X, into kernel space by computing the Gram Matrix, K, where each element k ij is the dot product in kernel space. using

8 Kernel K-means The Voronoi region and Voronoi Set in the feature space are redefined as: and Algorithm: 1. Project the data set X into a feature space F, by means of a nonlinear mapping 2. Initialize the codebook with 3. Compute for each center the set the set 4. Update the code vectors in 5. Go to step 3 until any changes 6. Return the feature space codebook.

9 Kernel K-means Continued Since forward is not explicitly known updating the code vectors is not straight Writing each centroid in Kernel space where is 1 if x h belongs to the set j, zero otherwise. Now, can be expanded to: Gram Matrix, ideally has a block diagonal structure if the clusters are uniformly dense and hence provide a good way to estimate the number of clusters too

10 Kernel K-means Examples Input Data Iris Data Fuzzy Membership After Clustering Gram Matrix After Reordering Eigenvalues of Gram Matrix Performance Eigenvalues of Gram Mat with RBF = 0.5 showing three major clusters M. Girolami, Mercer kernel based clustering in feature space, IEEE Trans. Neural Networks 13 (3) (2002)

11 Self Organizing Map(SOM) Code vectors organized on a grid and their adaptation is propagated along the grid Some popular metrics for the map include the Manhattan distance where the distance between two elements r = (r 1, r 2 ) and s = (s 1, s 2 ) is: Algorithm 1. Initialize the codebook V randomly picking from X 2. Initialize the set C of connections to form the rectangular grid of dimension n 1 n 2 3. Initialize t = 0 4. Randomly pick an input x from X. 5. Determine the winner: 6. Adapt each code vector: 7. Increment t 8. If t < t max go to step 4

12 SOM Example A SOM showing U.S. Congress voting patterns. The data were initially distributed randomly on a 2D grid and then clustered. The grey dots show the neurons. The first box shows clustering and, the second distances. The third is a panel shows the party affiliation, red-republican and bluedemocrat and the rest are the features, which, in this instance are yes(blue) or no(red) votes. Source: Schematic of A Self Organizing Map Source:

13 Kernel SOM Again the algorithm is adapted by first mapping the points to kernel space. The code vectors are defined as: (1) The winner is computed with: or The update rules are: Using (1) we get

14 Kernel SOM Example r 1 r 2 Neuron 2 r 1 - Distance from Neuron 1 in Hilbert Space r 2 - Distance from Neuron 2 in Hilbert Space Neuron 1 Input data clustered by Kernel SOM on the right Data clustered by Kernel SOM, using an RBF of 0.1 and 2 clusters D. Macdonald, C. Fyfe, The kernel self-organising map, in: Fourth International Conference on Knowledge-Based Intelligent Engineering Systems and Allied Technologies 2000, vol. 1, 2000, pp

15 Neural Gas and Kernel Neural Gas Similar to SOM the major difference being a soft adaptation rule in which all neurons are adapted to each individual input. ρ j is the rank of closeness of the current code vector j, to the input x λ is the characteristic decay For the kernelized version the update rule is:

16 Fuzzy C-Means Starts by defining a membership matrix, A cn denotes vector space of c x n real matrices; Minimizes functional: with the constraint m controls the fuzziness of the memberships and is usually set close to 2, if m tends to 1, the solution tends to the k-means solution Lagrangian of the objective is Taking the derivative with respect to u ih and v i and setting them to zero yields the iteration scheme:,

17 Kernel Fuzzy C-Means The objective in the kernel space is: In case of the Gaussian Kernel the derivative is: This yields the iteration scheme:,

18 Possibilistic C-Means Here, the class membership of a data point can be high for more than one class Objective that is minimized is: The iteration scheme is: For the parameter η i the authors suggest using:,

19 Kernel Possibilistic C-Means Kernelization of the metric in the objective yields: Minimization yields the iteration scheme: For the Gaussian Kernel:

20 One Class Support Vector Machines The idea is to find the smallest enclosing sphere in kernel space of radius R centered at v:,ξ i 0, are the slack variables The Lagrangian for the above is: Lagrange multipliers,,β i 0 and μ i 0 are is the penalty term with C -user defined const. Taking the derivative wrt ξ j, R, v and the KKT complementarity conditions yield the following QP: ξ i > 0, for outliers and ξ i = 0, 0 < β i < C for the support vectors

21 Example of one class SVMs One class SVM with a linear kernel applied to a data set with outliers. The gray line shows the projection in input space of the smallest enclosing sphere in feature space

22 Extension of one class SVMs to Clustering Similar to Kernel SVM but here the SVMs are applied to partition the space. The Voronoi regions are now spheres: Algorithm: 1. Project the data set X into a feature space, by means of a nonlinear mapping 2. Initialize the codebook with 3. Compute for each center 4. Apply One Class SVM to each and assign the center obtained to 5. Go to step 2 until any changes. 6. Return the feature space codebook.

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