Optimization Problems in Machine Learning

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1 Optimization Problems in Machine Learning Katya Scheinberg Lehigh University 2/15/12 EWO Seminar 1

2 Binary classification problem Two sets of labeled points - + 2/15/12 EWO Seminar 2

3 Binary classification problem How to label this new point? - + 2/15/12 EWO Seminar 3

4 Binary classification problem Probably green - + 2/15/12 EWO Seminar 4

5 Binary classification problem - + What about this one? 2/15/12 EWO Seminar 5

6 Binary classification problem - + Or this one? 2/15/12 EWO Seminar 6

7 Examples from image classification l Optical character recognition l Automatically read digits in zip code l 256 dim vector of pixels, 10 classes, l classification or clustering task l Face recognition and detection l much larger dimension, nonlinear representation, l Non-euclidean similarity measures 2/15/12 EWO Seminar 7

8 Examples from text and internet l Text categorization l detect spam/nonspam s l Many possible features l l False positives are very bad, false negatives are OK. l Online setting possible, huge data sets. choose articles of interest to individualize news sites l Large dimension size of dictionary, small training set, possibly online setting l Only few words are important. l Ranking l Predict a page rank for a given a search query l How to do it? Predict relative ranks of each pair of pages? 2/15/12 EWO Seminar 8

9 l l Examples from Medicine Functional Magnetic resonance imaging l Uses a standard MRI scanner to acquire images of functionally meaningful brain activity l l l l Measures changes in blood oxygenation Non-invasive, no ionizing radiation Good combination of spatial / temporal resolution l Voxel sizes ~4mm l Time of Repetition (TR) ~1s About voxels are active and measured. Only a few (probably) contribute to what the subject is feeling during the experiment (anger, frustration, boredom..) Breast cancer risk patients l l l l Take several measurements of a patient and some basic characteristics an predict if the patient is at high risk Low dimensional, but very different attributes. Large scale data. May involve active learning additional labels obtained by involving more tests or a professional. KDD 2008 cup challenge 2/15/12 EWO Seminar 9 fmri image courtesy of fmri Research Columbia Unoversity

10 The binary classification problem 2/15/12 EWO Seminar 10

11 Example 1 SUPPORT VECTOR MACHINES 2/15/12 EWO Seminar 11

12 Linear classifier Idea: separate a space into two half-spaces - + 2/15/12 EWO Seminar 12

13 Linear classifier Like this: - + 2/15/12 EWO Seminar 13

14 Linear classifier (0,1) - + w (1, 0) 2/15/12 EWO Seminar 14

15 Linear classifier (0,1) - + w (1, 0) 2/15/12 EWO Seminar 15

16 Linear classifier - + 2/15/12 EWO Seminar 16

17 Support vector machines - + Find the largest r or the smallest w 2/15/12 EWO Seminar 17

18 Support vector machines - + 2/15/12 EWO Seminar 18

19 Optimization Problem How many variables? Constraints? What can go wrong? 2/15/12 EWO Seminar 19

20 Support vector machines - + 2/15/12 EWO Seminar 20

21 Soft margin SVM How many variables? Constraints? 2/15/12 EWO Seminar 21

22 Soft margin SVM No constraints, but nonsmooth objective What if n is very large? What if m is very large? 2/15/12 EWO Seminar 22

23 Oh, no! What do we do now? /15/12 EWO Seminar 23

24 Kernel SVM /15/12 EWO Seminar 24

25 Kernel SVM /15/12 EWO Seminar 25

26 Example 2 COLLABORATIVE FILTERING, NETFLIX CHALLENGE 2/15/12 EWO Seminar 26

27 l Some users rate some movies they watched (or didn t!) l Predict the rating (1..5) for each user/ movie pair. l Use this prediction to recommend users the movies that they would like 2/15/12 EWO Seminar 27

28 Matrix completion problem, collaborative filtering Collaborative filtering: famous Netflix challenge Will user i like movie j? Complete the matrix based on partially filled information. 2/15/12 EWO Seminar 28

29 Linear factor model 2/15/12 EWO Seminar 29

30 Convex relaxation via nuclear norm l Given the values for a subset of entries, find the matrix with these entries and the smallest (or given) rank. l NP-hard problem. 2/15/12 EWO Seminar 30

31 Convex relaxation via nuclear norm l Given the values for a subset of entries, find the matrix with these entries and the smallest nuclear norm. l Convex problem 2/15/12 EWO Seminar 31

32 Convex relaxation via nuclear norm l Given the values for a subset of entries, find the matrix with similar entries and the smallest nuclear norm. l Or 2/15/12 EWO Seminar 32

33 SPARSE REGRESSION, LASSO 2/15/12 EWO Seminar 33

34 Least Squares Linear Regression 2/15/12 EWO Seminar 34

35 Disease state prediction 2/15/12 EWO Seminar 35

36 Least squares problem Standard form of LS problem A has columns and 5000 rows underdetermined. Regularized regression can be used x is going to be dense hence linear combination of all factors (genes) We would prefer to find a linear combinations of as few genes as possible 2/15/12 EWO Seminar 36

37 Lasso and other formulations to recover structure Sparse regularized regression or Lasso: Sparse regressor selection Noisy signal recovery 2/15/12 EWO Seminar 37

38 SPARSE INVERSE COVARIANCE SELECTION 2/15/12 EWO Seminar 38

39 Sparse inverse covariance selection 2/15/12 EWO Seminar 39

40 Optimizing log likelihood 2/15/12 EWO Seminar 40

41 Enforcing sparsity l Convex relaxation l Convex optimization problem with unique solution for each ½ 2/15/12 EWO Seminar 41

42 SOLUTION APPROACHES 2/15/12 EWO Seminar 42

43 Examples Lasso SVM Collaborative filtering Robust PCA SICS

44 Alternating directions (splitting) method Consider: Relax constraints via Augmented Lagrangian technique In our examples f(x) and g(y) are both such that the above functions are easy to optimize in x or y 2/15/12 EWO Seminar 44

45 A variant of alternating directions method This turns out to be equivalent to 2/15/12 EWO Seminar Goldfarb, Ma and S, 10 45

46 Alternating linearization method (ALM) 2/15/12 EWO Seminar 46 Goldfarb, Ma, S, 10

47 What is involved? l Theoretical convergence guarantees and convergence rates have been developed l The real complexity depends on the choice of µ l Various strategies for parameter selection affect performance and have extra costs. l Depending on application minimization and gradient computations can be expensive. l Inexact computations may be utilized but may lead to worse convergence properties. l Parallelization? Stochastic sampling? 2/15/12 EWO Seminar 47

48 THANK YOU! 2/15/12 EWO Seminar 48

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