Non-linear Support Vector Machines

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1 Non-linear Support Vector Machines Andrea Passerini Machine Learning

2 Non-linear Support Vector Machines Non-linearly separable problems Hard-margin SVM can address linearly separable problems Soft-margin SVM can address linearly separable problems with outliers Non-linearly separable problems need a higher expressive power (i.e. more complex feature combinations) We do not want to loose the advantages of linear separators (i.e. large margin, theoretical guarantees) Solution Map input examples in a higher dimensional feature space Perform linear classification in this higher dimensional space

3 Non-linear Support Vector Machines feature map Φ : X H Φ is a function mapping each example to a higher dimensional space H Examples x are replaced with their feature mapping Φ(x) The feature mapping should increase the expressive power of the representation (e.g. introducing features which are combinations of input features) Examples should be (approximately) linearly separable in the mapped space

4 Feature map Homogeneous (d = 2) ( x1 Φ x 2 ) = Polynomial mapping x 2 1 x 1 x 2 x 2 2 Φ ( x1 x 2 Inhomogeneous (d = 2) ) = x 1 x 2 x 2 1 x 1 x 2 x 2 2 Maps features to all possible conjunctions (i.e. products) of features: 1 of a certain degree d (homogeneous mapping) 2 up to a certain degree (inhomogeneous mapping)

5 Feature map Φ

6 Non-linear Support Vector Machines f(x) Linear separation in feature space SVM algorithm is applied just replacing x with Φ(x): f (x) = w T Φ(x) + w 0 A linear separation (i.e. hyperplane) in feature space corresponds to a non-linear separation in input space, e.g.: ( ) x1 f = sgn(w 1 x1 2 + w 2x 1 x 2 + w 3 x2 2 + w 0) x 2

7 Support Vector Regression Rationale Retain combination of regularization and data fitting Regularization means smoothness (i.e. smaller weights, lower complexity) of the learned function Use a sparsifying loss to have few support vector

8 Support Vector Regression ɛ-insensitive loss l(f (x), y) = y f (x) ɛ = { 0 if y f (x) ɛ y f (x) ɛ otherwise Tolerate small (ɛ) deviations from the true value (i.e. no penalty) Defines an ɛ-tube of insensitiveness around true values This also allows to trade off function complexity with data fitting (playing on ɛ value)

9 Support Vector Regression Optimization problem Note 1 min w X,w 0 IR, ξ,ξ IR m 2 w 2 + C subject to (ξ i + ξi ) w T Φ(x i ) + w 0 y i ɛ + ξ i y i (w T Φ(x i ) + w 0 ) ɛ + ξ i ξ i, ξ i 0 Two constraints for each example for the upper and lower sides of the tube Slack variables ξ i, ξi penalize predictions out of the ɛ-insensitive tube

10 Support Vector Regression Lagrangian We include constraints in the minimization function using Lagrange multipliers (α i, α i, β i, β i 0): L = 1 m 2 w 2 + C (ξ i + ξi ) (β i ξ i + βi ξ i ) α i (ɛ + ξ i + y i w T Φ(x i ) w 0 ) αi (ɛ + ξ i y i + w T Φ(x i ) + w 0 )

11 Support Vector Regression Dual formulation Vanishing the derivatives wrt the primal variables we obtain: L m w = w (αi α i )Φ(x i ) = 0 w = L w 0 = (α i αi ) = 0 L ξ i = C α i β i = 0 α i [0, C] L = C αi βi = 0 αi [0, C] ξ i (αi α i )Φ(x i )

12 Support Vector Regression Dual formulation Substituting in the Lagrangian we get: ɛ (αi α i )(αj α j )Φ(x i ) T Φ(x j ) i,j=1 } {{ } w 2 ξ i (C β i α i ) + }{{} =0 m (α i + αi ) + ξ i (C β i α i ) }{{} =0 y i (α i α i ) + w 0 (αi α i )(αj α j )Φ(x i ) T Φ(x j ) i,j=1 m (α i α i ) } {{ } =0

13 Support Vector Regression Dual formulation max α IR m 1 2 subject to ɛ (αi α i )(αj α j )Φ(x i ) T Φ(x j ) i,j=1 (αi + α i ) + (α i αi ) = 0 y i (αi α i ) α i, α i [0, C] i [1, m] Regression function f (x) = w T Φ(x) + w 0 = (α i αi )Φ(x i) T Φ(x) + w 0

14 Support Vector Regression Karush-Khun-Tucker conditions (KKT) At the saddle point it holds that for all i: α i (ɛ + ξ i + y i w T Φ(x i ) w 0 ) = 0 α i (ɛ + ξ i y i + w T Φ(x i ) + w 0 ) = 0 β i ξ i = 0 β i ξ i = 0 Combined with C α i β i = 0,α i 0,β i 0 and C α i β i = 0,α i 0,β i 0 we get α i [0, C] α i [0, C] and α i = C if ξ i > 0 α i = C if ξ i > 0

15 Support Vector Regression Support Vectors All patterns within the ɛ-tube, for which f (x i ) y i < ɛ, have α i, αi = 0 and thus don t contribute to the estimated function f. Patterns for which either 0 < α i < C or 0 < αi < C are on the border of the ɛ-tube, that is f (x i ) y i = ɛ. They are the unbound support vectors. The remaining training patterns are margin errors (either ξ i > 0 or ξi > 0), and reside out of the ɛ-insensitive region. They are bound support vectors, with corresponding α i = C or αi = C.

16 Support Vectors

17 Support Vector Regression: example for decreasing ɛ

18 References Non linear SVM C. Burges, A tutorial on support vector machines for pattern recognition, Data Mining and Knowledge Discovery, 2(2), , Support vector regression J.Shawe-Taylor and N. Cristianini, Kernel Methods for Pattern Analysis, Cambridge University Press, 2004 (Section 7.3.3)

19 Appendix Smallest enclosing hypersphere Support vector ranking

20 Smallest Enclosing Hypersphere Rationale Usage Characterize a set of examples defining boundaries enclosing them Find smallest hypersphere in feature space enclosing data points Account for outliers paying a cost for leaving examples out of the sphere One-class classification: model a class when no negative examples exist Anomaly/novelty detection: detect test data falling outside of the sphere and return them as novel/anomalous (e.g. intrusion detection systems, Alzheimer s patients monitoring)

21 Smallest Enclosing Hypersphere Optimization problem min R IR,o H,ξ IR m R 2 + C ξ i subject to Φ(x i ) o 2 R 2 + ξ i i = 1,..., m ξ i 0, i = 1,..., m Note o is the center of the sphere R is the radius which is minimized slack variables ξ i gather costs for outliers

22 Smallest Enclosing Hypersphere Lagrangian (α i, β i 0) L = R 2 + C ξ i α i (R 2 + ξ i Φ(x i ) o 2 ) β i ξ i Vanishing the derivatives wrt primal variables L m R = 2R(1 α i ) = 0 α i = 1 L m o = 2 α i (Φ(x i ) o)( 1) = 0 o L ξ i = C α i β i = 0 α i [0, C] α i }{{} =1 = α i Φ(x i )

23 Smallest Enclosing Hypersphere Dual formulation R 2 (1 + α i ) + } {{ } =0 α i (Φ(x i ) ξ i (C α i β i ) }{{} =0 α j Φ(x j )) T (Φ(x i ) j=1 }{{} o α h Φ(x h )) h=1 }{{} o

24 Smallest Enclosing Hypersphere Dual formulation α i (Φ(x i ) = = α j Φ(x j )) T (Φ(x i ) j=1 α i Φ(x i ) T Φ(x i ) m α i j=1 α i Φ(x i ) T α j Φ(x j ) T Φ(x i ) + α i Φ(x i ) T Φ(x i ) α h Φ(x h )) = h=1 m h=1 α i j=1 }{{} =1 α i Φ(x i ) T α h Φ(x h ) α j Φ(x j ) T m j=1 α j Φ(x j ) m h=1 α h Φ(x h ) =

25 Smallest Enclosing Hypersphere Dual formulation max α IR m subject to Distance function α i Φ(x i ) T Φ(x i ) α i α j Φ(x i ) T Φ(x j ) i,j=1 α i = 1, 0 α i C, i = 1,..., m. The distance of a point from the origin is: R 2 (x) = Φ(x) o 2 = (Φ(x) α i Φ(x i )) T (Φ(x) = Φ(x) T Φ(x) 2 α j Φ(x j )) j=1 α i Φ(x i ) T Φ(x) + α i α j Φ(x i ) T Φ(x j ) i,j=1

26 Smallest Enclosing Hypersphere Karush-Khun-Tucker conditions (KKT) At the saddle point it holds that for all i: Support vectors β i ξ i = 0 α i (R 2 + ξ i Φ(x i ) o 2 ) = 0 Unbound support vectors (0 < α i < C), whose images lie on the surface of the enclosing sphere. Bound support vectors (α i = C), whose images lie outside of the enclosing sphere, which correspond to outliers. All other points (α = 0) with images inside the enclosing sphere.

27 Smallest Enclosing Hypersphere

28 Smallest Enclosing Hypersphere Decision function The radius R of the enclosing sphere can be computed using the distance function on any unbound support vector x: R 2 (x) = Φ(x) T Φ(x) 2 α i Φ(x i ) T Φ(x)+ α i α j Φ(x i ) T Φ(x j ) i,j=1 A decision function for novelty detection could be: f (x) = sgn (R 2 (x) (R ) 2) i.e. positive if the examples lays outside of the sphere and negative otherwise

29 Support Vector Ranking Rationale Order examples by relevance (e.g. urgence, movie rating) Learn scoring function predicting quality of example Constraint function to score x i higher than x j if it is more relevant (pairwise comparisons for training) Easily formalized as a support vector classification task

30 Support Vector Ranking Optimization problem min w X,w 0 IR, ξ i,j IR subject to 1 2 w 2 + C i,j ξ i,j w T Φ(x i ) w T Φ(x j ) 1 ξ i,j ξ i,j 0 i, j : x i x j Note There is one constraint for each pair of examples having ordering information (x i x j means the former is comes first in the ranking) Examples should be correctly ordered with a large margin

31 Support Vector Ranking Support vector classification on pairs min w X,w 0 IR, ξ i,j IR 1 2 w 2 + C i,j ξ i,j subject to y i,j w T (Φ(x i ) Φ(x j )) 1 ξ i,j }{{} Φ(x ij ) ξ i,j 0 i, j : x i x j where labels are always positive y i,j = 1

32 Support Vector Ranking Decision function f (x) = w T Φ(x) Standard support vector classification function (unbiased) Represents score of example for ranking it

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