Understanding Linear Learning Machines and SVM

1. Content Linear Learning Machines and SVM The Perceptron Algorithm revisited Functional and Geometric Margin Novikoff theorem Dual Representation Learning in the Feature Space Kernel-Induced Feature Space Making Kernels The Generalization Problem Probably Approximately Correct Learning Structural Risk Minimization 236607 Visual Recognition

2. Linear Learning Machines and SVM Basic Notations • Input space • Output space for classification for regression • Hypothesis • Training Set • Test error also R(a) • Dot product 236607 Visual Recognition

3. Basic Notations cont. “Learning machine” – any function estimation algorithm, “training” – parameter estimation procedure, “testing” – computation of function value, “performance” – generalization accuracy (i.e. error rate as test set size tends to infinity 236607 Visual Recognition

4. ThePerceptron Algorithmrevisited • Linear separation • of the input space • The algorithm requires that the input patterns are linearly separable, • which means that there exist linear discriminant function which has • zero training error. We assume that this is the case. 236607 Visual Recognition

5. ThePerceptron Algorithm(primal form) • initialize • repeat • error false • for i=1..l • if then error true end if end for until (error==false) return k,(wk,bk) where k is the number of mistakes 236607 Visual Recognition

6. ThePerceptron AlgorithmComments • The perceptron works by adding misclassified positive or subtracting misclassified negative examples to an arbitrary weight vector, which (without loss of generality) we assumed to be the zero vector. So the final weight vector is a linear combination of training points • where, since the sign of the coefficient of is given by label yi, the are positive values, proportional to the number of times, misclassification of has caused the weight to be updated. It is called the embedding strength of the pattern . 236607 Visual Recognition

7. Functional and Geometric Margin • The notion of margin of a data point w.r.t. a linear discriminant will turn out to be an important concept. • The functional margin of a linear discriminant (w,b) w.r.t. a labeled pattern is defined as • If the functional margin is negative, then the pattern is incorrectly classified, if it is positive then the classifier predicts the correct label. • The larger the further away xi is from the discriminant. • This is made more precise in the notion of the geometric margin 236607 Visual Recognition

8. Functional and Geometric Margin cont. The geometric margin of The margin of a training set two points 236607 Visual Recognition

9. Functional and Geometric Margin cont. • which measures the Euclidean distance of a point from the decision boundary. • Finally, is called the (functional) margin of (w,b) • w.r.t. the data set S={(xi,yi)}. • The margin of a training set S is the maximum geometric margin over all hyperplanes. A hyperplane realizing this maximum is a maximal margin hyperplane. • Maximal Margin Hyperplane 236607 Visual Recognition

10. Novikoff theorem • Theorem: • Suppose that there exists a vector and a bias term such that the margin on a (non-trivial) data set S is at least , i.e. • then the number of update steps in the perceptron algorithm is at most • where 236607 Visual Recognition

11. Novikoff theorem cont. • Comments: • Novikoff theorem says that no matter how small the margin, if a data set is linearly separable, then the perceptron will find a solution that separates the two classes in a finite number of steps. • More precisely, the number of update steps (and the runtime) will depend on the margin and is inverse proportional to the squared margin. • The bound is invariant under rescaling of the patterns. • The learning rate does not matter. 236607 Visual Recognition

12. Dual Representation • The decision function can be rewritten as follows: • And also the update rule can be rewritten as follows: • The learning rate only influence the overall scaling of the hyperplanes, it does no effect algorithm with zero starting vector, so we can put 236607 Visual Recognition

13. Duality: First Property of SVMs • DUALITYis the first feature of Support Vector Machines • SVM are Linear Learning Machines represented in a dual fashion • Data appear only inside dot products (in decision • function and in training algorithm) • The matrix is called Gram matrix 236607 Visual Recognition

14. Limitations of Linear Classifiers • Linear Learning Machines (LLM) cannot deal with • Non-linearly separable data • Noisy data • This formulation only deals with vectorial data 236607 Visual Recognition

15. Limitations of Linear Classifiers • Neural networks solution: multiple layers of thresholded linear functions – multi-layer neural networks. Learning algorithms – back-propagation. • SVM solution: kernel representation. • Approximation-theoretic issues are independent of the learning-theoretic ones. Learning algorithms are decoupled from the specifics of the application area, which is encoded into design of kernel. 236607 Visual Recognition

16. Learning in the Feature Space • Map data into a feature space where they are linearly separable (i.e. attributes features) 236607 Visual Recognition

17. Learning in the Feature Space cont. • Example • Consider the target function • giving gravitational force between two bodies. • Observable quantities are masses m1, m2 and distance r. A linear machine could not represent it, but a change of coordinates • gives the representation 236607 Visual Recognition

18. Learning in the Feature Space cont. • The task of choosing the most suitable representation is known as feature selection. • The space X is referred to as the input space, while • is called the feature space. • Frequently one seeks to find smallest possible set of features that still conveys essential information (dimensionality reduction 236607 Visual Recognition

19. Problems with Feature Space • Working in high dimensional feature spaces solves the problem of expressing complex functions • BUT: • There is a computational problem (working with very large vectors) • And a generalization theory problem (curse of • dimensionality) 236607 Visual Recognition

20. Implicit Mapping to Feature Space • We will introduce Kernels: • Solve the computational problem of working with many dimensions • Can make it possible to use infinite dimensions • Efficiently in time/space • Other advantages, both practical and conceptual 236607 Visual Recognition

21. Kernel-Induced Feature Space • In order to learn non-linear relations we select non-linear features. Hence, the set of hypotheses we consider will be functions of type • where is a non-linear map from input space to feature space • In the dual representation, the data points only appear inside dot products 236607 Visual Recognition

22. Kernels • Kernel is a function that returns the value of the dot product between the images of the two arguments • When using kernels, the dimensionality of space F not necessarily important. We may not even know the map • Given a function K, it is possible to verify that it is a kernel 236607 Visual Recognition

23. Kernels cont. • One can use LLMs in a feature space by simply rewriting it in dual representation and replacing dot products with kernels: 236607 Visual Recognition

24. The Kernel Matrix (Gram Matrix) • K= 236607 Visual Recognition

25. The Kernel Matrix • The central structure in kernel machines • Information ‘bottleneck’: contains all necessary information for the learning algorithm • Fuses information about the data AND the kernel • Many interesting properties: 236607 Visual Recognition

26. Mercer’s Theorem • The kernel matrix is Symmetric Positive Definite • Any symmetric positive definite matrix can be regarded as a kernel matrix, that is as an inner product matrix in some space • More formally, Mercer’s Theorem: Every (semi) positive definite, symmetric function is a kernel: i.e. there exists a mapping such that it is possible to write: • Pos.Def.: 236607 Visual Recognition

27. Mercer’s Theorem cont. • Eigenvalues expansion of Mercer’s Kernels: • That is: the eigenvalues act as features! 236607 Visual Recognition

28. Examples of Kernels • Simple examples of kernels are: 236607 Visual Recognition

29. Example: Polynomial Kernels 236607 Visual Recognition

30. Example: Polynomial Kernels 236607 Visual Recognition

31. Making Kernels • The set of kernels is closed under some operations. If • K,K’ are kernels, then: • K+K’ is a kernel • cK is a kernel, if c>0 • aK+bK’ is a kernel, for a,b>0 • Etc… • One can make complex kernels from simple ones: modularity! 236607 Visual Recognition

32. Second Property of SVMs: • SVMs are Linear Learning Machines, that : • Use a dual representation • Operate in a kernel induced feature space (that is: • is a linear function in the feature space implicitly defined by K) 236607 Visual Recognition

33. A bad kernel • .. Would be a kernel whose kernel matrix is mostly diagonal: all points orthogonal to each other, no clusters, no structure…. 236607 Visual Recognition

34. No Free Kernel • In mapping in a space with too many irrelevant features, kernel matrix becomes diagonal • Need some prior knowledge of target so choose a good kernel 236607 Visual Recognition

35. The Generalization Problem • The curse of dimensionality: easy to overfit in high dimensional spaces • (=regularities could be found in the training set that are accidental, that is that would not be found again in a test set) • The SVM problem is ill posed (finding one hyperplane that separates the data: many such hyperplanes exist) • Need principled way to choose the best possible hyperplane 236607 Visual Recognition

36. The Generalization Problem cont. • “Capacity” of the machine – ability to learn any training set without error. • “A machine with too much capacity is like a botanist with a photographic memory who, when presented with a new tree, concludes that it is not a tree because it has a different number of leaves from anything she has seen before; a machine with too little capacity is like the botanist’s lazy brother, who declares that if it’s green, it’s a tree” • C. Burges 236607 Visual Recognition

37. Probably Approximately Correct Learning • Assumptions and Definitions • Suppose: • We are given l observations • Train and test points drawn randomly (i.i.d) from some unknown probability distribution D(x,y) • The machine learns the mapping and outputs a hypothesis . A particular choice of • generates “trained machine”. • The expectation of the test error or “expected risk” is 236607 Visual Recognition

38. A Bound on the Generalization Performance • The “empirical risk” is: • Choose some such that . With probability the following bound holds (Vapnik,1995): • where is called VC dimension is a measure of “capacity” of machine. • R.h.s. of (3) is called the “risk bound” of h(x,a) in distribution D. 236607 Visual Recognition

39. A Bound on the Generalization Performance • The second term in the right-hand side is called VC confidence. • Three key points about the actual risk bound: • It is independent of D(x,y) • It is usually not possible to compute the left hand side. • If we know d, we can compute the right hand side. • This gives a possibility to compare learning machines! 236607 Visual Recognition

40. The VC Dimension • Definition: the VC dimension of a set of functions • is d if and only if there exists a set of points such that these points can be labeled in all 2d possible configurations, and for each labeling, a member of set H can be found which correctly assigns those labels, but that no set exists where q>d satisfying this property. 236607 Visual Recognition

41. The VC Dimension • Saying another way:VC dimension is size of largest subset of X shattered by H (every dichotomy implemented). VC dimension measures the capacity of a set H of functions. • If for any number N, it is possible to find N points • that can be separated in all the 2N possible ways, we will say that the VC-dimension of the set is infinite 236607 Visual Recognition

42. The VC Dimension Example • Suppose that the data live in space, and the set • consists of oriented straight lines, (linear discriminants). While it is possible to find three points that can be shattered by this set of functions, it is not possible to find four. Thus the VC dimension of the set of linear discriminants in is three. 236607 Visual Recognition

43. The VC Dimension cont. • Theorem 1Consider some set of m points in . Choose any one of the points as origin. Then the m points can be shattered by oriented hyperplanes if and only if the position vectors of the remaining points are linearly independent. • Corollary: The VC dimension of the set of oriented hyperplanes in is n+1, since we can always choose n+1 points, and then choose one of the points as origin, such that the position vectors of the remaining points are linearly independent, but can never choose n+2 points 236607 Visual Recognition

44. The VC Dimension cont. • VC dimension can be infinite even when the number of parameters of the set of hypothesis functions is low. • Example: • For any integer l with any labels • we can find l points and parameter a such that those points can be shattered by • Those points are: • and parameter a is: 236607 Visual Recognition

45. Minimizing the Bound by Minimizing d 236607 Visual Recognition

46. Minimizing the Bound by Minimizing d • VC confidence (second term in (3)) dependence on d/l given 95% confidence level ( ) and assuming training sample of size 10000. • One should choose that learning machine whose set of functions has minimal d • For d/l>0.37 (forand l=10000) VC confidence >1. Thus for higher d/l the bound is not tight. 236607 Visual Recognition

47. Example • Question. What is VC dimension and empirical risk of the nearest neighbor classifier? • Any number of points, labeled arbitrarily, will be successfully learned, thus and empirical risk =0 . • So the bound provide no information in this example. 236607 Visual Recognition

48. Structural Risk Minimization • Finding a learning machine with the minimum upper bound on the actual risk leads us to a method of choosing an optimal machine for a given task. This is the essential idea of the structural risk minimization (SRM). • Let be a sequence of nested subsets of hypotheses whose VC dimensions satisfy • d1 < d2 < d3 < … SRM then consists of finding that subset of functions which minimizes the upper bound on the actual risk. This can be done by training a series of machines, one for each subset, where for a given subset the goal of training is to minimize the empirical risk. One then takes that trained machine in the series whose sum of empirical risk and VC confidence is minimal. 236607 Visual Recognition