A GENERAL FORMULATION FOR SUPPORT VECTOR MACHINES. Wei Chu, S. Sathiya Keerthi, Chong Jin Ong

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1 A GENERAL FORMULATION FOR SUPPORT VECTOR MACHINES Wei Chu, S. Sathiya Keerthi, Chong Jin Ong Control Division, Department of Mechanical Engineering, National University of Singapore 0 Kent Ridge Crescent, Singapore, 960 engp9354@nus.edu.sg, mpessk@nus.edu.sg, mpeongcj@nus.edu.sg ABSTRACT In this paper, we derive a general formulation of support vector machines for classification and regression respectively. L e loss function is proposed as a patch of L and L soft margin loss functions for classifier, while soft insensitive loss function is introduced as the generalization of popular loss functions for regression. The introduction of the two loss functions results in a general formulation for support vector machines.. INTRODUCTION As computationally powerful tools for supervised learning, support vector machines (SVMs) are widely used in classification and regression problems [0]. Let us suppose that a data set D = (x i, y i ) i =,..., n} is given for training, where the input vector x i R d and y i is the target value. SVMs take the idea to map these input vectors into a high dimensional reproducing kernel Hilbert space (RKHS), where a linear machine is constructed by imizing a regularized functional. The linear machine takes the form of f(x) = w φ(x) + b, where φ( ) is the mapping function, b is known as the bias, and the dot product φ(x) φ(x ) is also the reproducing kernel K(x, x ) in the RKHS. The regularized functional is usually defined as R(w, b) = C l ( y i, f(x i ) ) + w () where the regularization parameter C > 0, the norm of w in the RKHS is the stabilizer and n l( y i, f(x i ) ) is empirical loss term. In standard SVMs, the regularized functional () can be imized by solving a convex quadratic programg optimization problem that guarantees a unique global imum solution. Various loss functions can be used in SVMs that results in quadratic programg. In SVMs for classification [], hard margin, L and L loss functions are widely used. For regression, a lot of common loss Wei Chu gratefully acknowledges the financial support provided by the National University of Singapore through Research Scholarship. functions have been discussed in [9], such as Laplacian, Huber s, ɛ-insensitive and Gaussian etc. In this paper, we generalize these popular loss functions and put forward new loss functions for classification and regression respectively. The two new loss functions are C smooth. By introducing these loss functions in the regularized functional of classical SVMs in place of popular loss functions, we derive a general formulation for SVMs.. SUPPORT VECTOR CLASSIFIER In classical SVMs for binary classification (SVC) in which the target values y i, +}, the hard margin loss function is defined as l h (y x f x ) = 0 if y x f x +; + otherwise. The hard margin loss function is suitable for noise-free data sets. For other general cases, a loss function is popularly used in classical SVC, which is defined as l ρ (y x f x ) = 0 if y x f x +; ρ ( y x f x ) ρ otherwise, (3) where ρ is a positive integer. The imization of the regularized functional () with the (3) as loss function leads to a convex programg problem for any positive integer ρ; for L (ρ = ) or L (ρ = ), it is also a convex quadratic programg problem... L e Loss Function We generalize the L and L loss functions as the L e loss function, which is defined as 0 if y x f x > ; ( y l e (y x f x ) = x f x ) if y x f x ɛ; 4ɛ ( y x f x ) ɛ otherwise, (4) where the parameter ɛ > 0. ()

2 L L e Soft Margin Loss Functions in SVC L ε y x f x Figure : Graphs of loss functions, where ɛ is set at. From their definitions and Figure, we find that the L e approaches to L as the parameter ɛ 0. Let ɛ be fixed at some large value, the L e approaches the L for all practical purposes... Optimization Problem The imization problem in SVC () with the L e loss function can be rewritten as the following equivalent optimization problem by introducing slack variables ξ i y i ( w φ(x i ) + b ) i, which we refer to as the primal problem where R(w, b, ξ) = C n ( ) ψ e ξi + w,b,ξ w (5) yi ( w φ(x i ) + b ) ξ i, i ξ i 0, i ξ ψ e (ξ) = if ξ [0, ɛ]; 4ɛ ξ ɛ if ξ (ɛ, + ). Standard Lagrangian techniques [4] are used to derive the dual problem. Let i 0 and γ i 0 be the corresponding Lagrange multipliers for the inequalities in the primal problem (6), and then the Lagrangian for the primal (6) (7) problem would be: L(w, b, ξ) = C n ψ ( ) e ξi + w n γ i ξ i i (y ) i ( w φ(x i ) + b) + ξ i (8) The KKT conditions for the primal problem (5) require w = y i i φ(x i ) (9) y i i = 0 (0) C ψ e(ξ i ) ξ i = i + γ i i () Based on the definition of ψ e ( ) given in (7) and the constraint condition (), an equality constraint on Lagrange multipliers can be explicitly written as C ξi ɛ = i + γ i if 0 ξ i ɛ C = i + γ i if ξ i > ɛ i () If we collect all terms involving ξ i in the Lagrangian (8), we get T i = Cψ e (ξ i ) ( i + γ i )ξ i. Using (7) and () we can rewrite T i as ɛ T i = C ( i + γ i ) if ξ [0, ɛ]; (3) Cɛ if ξ (ɛ, + ). Thus the ξ i can be eliated if we set T i = ɛ C ( i + γ i ) and introduce the additional constraints 0 i + γ i C. Then the dual problem can be stated as a maximization problem in terms of the positive dual variables i and γ i : n max R(, γ) = i ɛ ( i + γ i ),γ C i y i j y j φ(x i ) φ(x j ) (4) i 0, γ i 0, 0 i + γ i C, i and i y i = 0. (5) It is noted that R(, γ) R(, 0) for any and γ satisfying (5). Hence the maximization of (4) over (, γ) can be found as maximizing R(, 0) over 0 i C and n i y i = 0. Therefore, the dual problem can be finally simplified as i y i j y j K(x i, x j ) i + ɛ C i (6)

3 0 i C, i and n i y i = 0. With the equality (9), the linear classifier can be obtained from the solution of (6) as f(x) = n i y i K(x i, x)+b where b can be easily obtained as a byproduct in the solution. In most of the cases, only some of the Lagrange multipliers, i, differ from zero at the optimal solution. They define the support vectors (SVs) of the problem. More exactly, the training samples (x i, y i ) associated with i satisfying 0 < i < C are called off-bound SVs, the samples with i = C are called on-bound SVs, and the samples with i = 0 are called non-svs..3. Discussion The above formulation (6) is a general framework for classical SVC. There are three special cases of the formulation:. L : the formulation is just the SVC using L if we set ɛ = 0.. Hard margin: when we set ɛ = 0 and keep C large enough to prevent any i from reaching the upper bound C, the solution of this formulation is identical to the standard SVC with hard margin loss function. 3. L : when we set C equal to the regularization parameter in SVC with L, and ɛ keep C large enough to prevent any i from reaching the upper bound at the optimal solution, the solution will be same as that of the standard SVC with L soft margin loss function. In practice, such as on unbalanced data sets, we would like to use different regularization parameters C + and C for the samples with positive label and negative label separately. As an extremely general case, we can use different regularization parameter C i for every sample (x i, y i ). It is straightforward to obtain the general dual problem as i y i j y j K(x i, x j ) i + ɛ C i n i (7) 0 i C i, i and n i y i = 0. Obviously, the dual problems (7) is a constrained convex quadratic programg problem. Denoting ˆ = [y, y,..., y n n ], P = [ y, y,..., y n ] T and Q = K + Λ where Λ is a n n diagonal matrix with Λ ii = ɛ C i and K is the kernel matrix with K ij = K(x i, x j ), (7) can be written in a general form as ˆ ˆT Q ˆ + P T ˆ (8) The ratio between C + and C is usually fixed at C + = N, where C N + N + is the number of samples with positive label and N is the number of samples with negative label. l i ˆ i u i, i and n ˆ i = 0 where l i = 0, u i = C i when y i = + and l i = C i, u i = 0 when y i =. As to the algorithm design for the solution, matrix-based quadratic programg techniques that use the chunking idea can be employed here. Popular SMO algorithms [7, 5] could be easily adapted for the solution. For a program design and source code, refer to []. 3. SUPPORT VECTOR REGRESSION A lot of loss functions for support vector regression (SVR) have been discusses in [9] that leads to a general optimization problem. There are four popular loss functions widely used for regression problems. They are. Laplacian loss function: l l (δ) = δ.. Huber s loss function: l h (δ) = δ 4ɛ δ ɛ if δ ɛ otherwise. 3. ɛ-insensitive loss function: 0 if δ ɛ l ɛ (δ) = δ ɛ otherwise. 4. Gaussian loss function: l g (δ) = δ. We introduce another loss function as the generalization of these popular loss functions. 3.. Soft Insensitive Loss Function The loss function known as soft insensitive loss function (SILF) is defined as: l ɛ,β (δ) = δ ɛ if δ > ( + β)ɛ ( δ ( β)ɛ) 4βɛ if ( + β)ɛ δ ( β)ɛ 0 if δ < ( β)ɛ (9) where 0 < β and ɛ > 0. There is a profile of SILF as shown in Figure. The properties of SILF are entirely controlled by two parameters, β and ɛ. For a fixed ɛ, SILF approaches the ɛ-ilf as β 0; on the other hand, as β, it approaches the Huber s loss function. In addition, SILF becomes the Laplacian loss function as ɛ 0. Hold ɛ fixed at some large value and let β, the SILF approach the quadratic loss function for all practical purposes. The application of SILF in Bayesian SVR has been discussed in [3]. 3.. Optimization Problem We introduce SILF into the regularized functional () that will leads to a quadratic programg problem that could work as a general framework. As usual, two slack variables

4 Soft Insensitive Loss Function C ψ e(ξi ) ξi = i + γi i (6) Based on the definition of ψ s given by (), the constraint condition (6) could be explicitly written as equality constraints: ξ i i + γ i = C βɛ if 0 ξ i βɛ i + γ i = C if ξ i > βɛ (7) (+β)ε 0.5 ( β)ε 0 ( β)ε ε=0.5 (+β)ε.5 Figure : Graphs of soft insensitive loss functions, where ɛ = 0.5 and β = 0.5. ξ i and ξ i are introduced as ξ i y i w φ(x i ) b ( β)ɛ and ξ i w φ(x i) +b y i ( β)ɛ i. The imization of the regularized functional () with SILF as loss function could be rewritten as the following equivalent optimization problem, which is usually called primal problem: R(w, b, ξ, w,b,ξ,ξ ξ ) = C ( ψs (ξ i )+ψ s (ξi ) ) + w (0) y i w φ(x i ) b ( β)ɛ + ξ i ; w φ(x i ) + b y i ( β)ɛ + ξi ; () ξ i 0; ξi 0 i where ξ if ξ [0, βɛ]; ψ s (ξ) = 4βɛ ξ βɛ if ξ (βɛ, + ). () Let i 0, i 0, γ i 0 and γ i 0 i be the corresponding Lagrangian multipliers for the inequalities in (). The KKT conditions for the primal problem require w = ( i i ) φ(x i ) (3) ( i i ) = 0 (4) C ψ e(ξ i ) ξ i = i + γ i i (5) ξ i i + γi = C βɛ if 0 ξi βɛ (8) i + γ i = C if ξ i > βɛ Following the analogous arguments as SVC did in (3), we can also eliate ξ i and ξi here. That yields a maximization problem in terms of the positive dual variables w, b, ξ, ξ : max ( i,γ,,γ i )( j i ) φ(x i ) φ(x j ) + ( i i )y i ( i + i )( β)ɛ βɛ C ( (i + γ i ) + (i + γi ) ) (9) i 0, i 0, γ i 0, γi 0, 0 i + γ i C, i, 0 i + γ i C, i and n ( i i ) = 0. As γ i and γi only appear in the last term in (9), the functional (9) is maximal when γ i = 0 and γi = 0 i. Thus, the dual problem can be simplified as R(, ) = ( i, i )( j i )K(x i, x j ) ( i i )y i + ( i + i )( β)ɛ + βɛ C ( i + i ) (30) 0 i C, i, 0 i C, i and n ( i i ) = 0. With the equality (3), the dual form of the regression function can be written as f(x) = n ( i i )K(x i, x j ) + b Discussion Like SILF, the dual problem (30) is a generalization of the several SVR formulations. More exactly,. when we set β = 0, (30) becomes the classical SVR using ɛ-insensitive loss function;. When β =, (30) becomes that when the Huber s loss function is used.

5 3. When β = 0 and ɛ = 0, (30) becomes that for the case of the Laplacian loss function. 4. As for the optimization problem () using Gaussian loss function with the variance σ, that is equivalent to the general SVR (30) with β = and ɛ C = σ provided that we keep upper bound C large enough to prevent any i and i from reaching the upper bound at the optimal solution. That is also the case of least-square support vector machines. As for a SMO algorithm design, refer to [6]. The dual problem (30) is also a constrained convex quadratic programg problem. Let us denote ˆ = [,..., n,,..., n] T, P = [ y + ( β)ɛ,..., y n + ( β)ɛ, [ y ( β)ɛ,..., y n ( β)ɛ] T K + βɛ Q = C I K ] K K + βɛ C I where K is the kernel matrix with ij-entry K(x i, x j ), (30) can be rewritten as ˆ ˆT Q ˆ + P T ˆ (3) l i ˆ i u i, i and n ˆ i = 0 where l i = 0, u i = C for i n and l i = C, u i = 0 for n + i n. 4. CONCLUSION In this paper, we introduce two loss functions as the generalization of popular loss functions for SVC and SVR respectively. The dual problem of SVMs arising from the introduction of the two loss functions could be used as a general formulation for SVMs. The optimization problems in SVMs with popular loss functions could be obtained as the special cases in the general formulation we derived. As a byproduct, the general formulation also provides a framework that facilitates the algorithm implementation. [3] Chu, W and Keerthi, S. S. and Ong, C. J., A unified loss function in Bayesian framework for support vector regression, Proceeding of the 8th International Conference on Machine Learning, mpessk/svm/icml.pdf [4] Fletcher, R., Practical methods of optimization, John Wiley, 987. [5] Keerthi, S. S. and Shevade, S. K. and Bhattacharyya, C. and Murthy, K. R. K., Improvements to Platt s SMO algorithm for SVM classifier design, Neural Computation, 3 pp , 00. [6] Keerthi, S. S. and Shevade, SMO algorithm for least squares SVM formulations, Technical Report CD-0-08, Dept. of Mechanical Engineering, National University of Singapore, mpessk/papers/lssvm smo.ps.gz [7] Platt, J. C., Fast training of support vector machines using sequential imal optimization, Advances in Kernel Methods - Support vector learning, pp , 998. [8] Shevade, S. K. and Keerthi, S. S. and Bhattacharyya, C. and Murthy, K. R. K., Improvements to the SMO algorithm for SVM regression, IEEE Transactions on Neural Networks, pp , 000. [9] Smola, A. J. and Schölkopf, B., A tutorial on support vector regression, Technical Report NC-TR , GMD First, October, 998. [0] Vapnik, V. N. The Nature of Statistical Learning Theory, New York: Springer-Verlag, REFERENCES [] Burges, C. J. C. A tutorial on support vector machines for pattern recogintion, Data Mining and Knowledge Discovery, () pp. -67, 998. [] Chu, W and Keerthi, S. S. and Ong, C. J., Extended SVMs - Theory and Implementation, Technical Report CD-0-4, Mechanical Engineering, National University of Singapore, chuwei/svm/esvm.pdf

An Improved Conjugate Gradient Scheme to the Solution of Least Squares SVM

An Improved Conjugate Gradient Scheme to the Solution of Least Squares SVM Wei Chu Chong Jin Ong chuwei@gatsby.ucl.ac.uk mpeongcj@nus.edu.sg S. Sathiya Keerthi mpessk@nus.edu.sg Control Division, Department