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RPTSVM

A Matlab code for recursive projection twin support vector machine. (You could Right-Click [Code] , and Save, then you can download the whole matlab code.)

Reference

Yuan-Hai Shao, Zhen Wang, Wei-Jie Chen, Nai-Yang Deng*. A regularization for the projection twin support vector machine[J]. Knowledge-Based Systems, 2013, 37: 203¨C210.

Main Function

function Predict_Y = RPTSVM(TestX,DataTrain,FunPara) %%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % RPTSVM: recursive projection twin support vector machine % % Predict_Y = RPTSVM(TestX,DataTrain,FunPara) % % Input: % TestX - Test Data matrix. Each row vector of fea is a data point. % % DataTrain - Struct value in Matlab(Training data). % DataTrain.A: Positive input of Data matrix. % DataTrain.B: Negative input of Data matrix. % % FunPara - Struct value in Matlab. The fields in options that can be set: % c1: [0,inf] Paramter to tune the weight. % c2: [0,inf] Paramter to tune the weight. % v1: [0,inf] Paramter to tune the weight. % v2: [0,inf] Paramter to tune the weight. % kerpara: Paramter of kernel function. % % Output: % Predict_Y - Predict value of the TestX. % % Examples: % DataTrain.A = rand(50,10); % DataTrain.B = rand(60,10); % TestX = rand(20,10); % FunPara.c1=0.1; % FunPara.c2=0.1; % FunPara.v1=0.1; % FunPara.v2=0.1; % FunPara.kerpara.type='rbf' % FunPara.kerpara.pars=1 % Predict_Y = RPTSVM(TestX,DataTrain,FunPara); % % Reference: % Yuan-Hai Shao, Zhen Wang, Wei-Jie Chen, Nai-Yang Deng*. % A regularization for the projection twin support vector machine[J]. % Knowledge-Based Systems,2013, 37: 203¨C210. % % Version 1.0 --Apr/2011 % Written by Yuan-Hai Shao (shaoyuanhai21@163.com) %%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Initailization %%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %tic; inputA = DataTrain.A; inputB = DataTrain.B; m = size(TestX,1); c1 = FunPara.c1; c2 = FunPara.c2; v1 = FunPara.v1; v2 = FunPara.v2; m1 = size(inputA,1); m2 = size(inputB,1); e1 = ones(m1,1); e2 = ones(m2,1); kerpara = FunPara.kerpara; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Compute w1 and w2 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%% if strcmp(kerpara.type,'rbf') X = [DataTrain.A;DataTrain.B]; inputA = kernelfun(inputA,kerpara,X); inputB = kernelfun(inputB,kerpara,X); TestX = kernelfun(TestX,kerpara,X); end s1 = m1*cov(inputA,1); s2 = m2*cov(inputB,1); I = eye(length(s1)); E1 = v1*s1 + I; E2 = v2*s2 + I; H = inputB-1/m1*e2*e1'*inputA; G = inputA-1/m2*e1*e2'*inputB; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %RPTWSVM1 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% HE = E1\H'; HHE = H*HE; HHE = (HHE+HHE')/2; alpha = qpSOR(HHE,0.5,c1,0.05); %SOR w1 = E1\H'*alpha; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %RPTWSVM2 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% GE = E2\G'; GGE = G*GE; GGE = (GGE+GGE')/2; beta = qpSOR(GGE,0.5,c2,0.05); %SOR w2 = E2\G'*beta; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %Record w1 and w2 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% W1 = w1; W2 = w2; center1 = mean(inputA,1); center2 = mean(inputB,1); %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %While multiple orthogonal recursive projection %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % mop=0; % while mop>0 % for i=1:m1 % inputA(i,:)=inputA(i,:)-(w1*inputA(i,:)*w1)'; % end % for i=1:m2 % inputB(i,:)=inputB(i,:)-(w2*inputB(i,:)*w2)'; % end % s1 = m1*cov(inputA,1); % s2 = m2*cov(inputB,1); % I = eye(length(s1)); % E1 = v1*s1 + I; % E2 = v2*s2 + I; % H = inputB-1/m1*e2*e1'*inputA; % G = inputA-1/m2*e1*e2'*inputB; % HE = E1\H'; % HHE = H*HE; % HHE = (HHE+HHE')/2; % alpha = qpSOR(HHE,0.5,c1,0.05); %SOR % w1 = E1\H'*alpha; % GE = E2\G'; % GGE = G*GE; % GGE = (GGE+GGE')/2; % beta = qpSOR(GGE,0.5,c2,0.05); %SOR % w2 = E2\G'*beta; % W1=[W1,w1]; % W2=[W2,w2]; % mop=mop-1; % end % toc %%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Predict and output %%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Predict_Y = zeros(m1,1); for i=1:m Y11(i,:)=TestX(i,:)*W1-center1*W1; Y22(i,:)=TestX(i,:)*W2-center2*W2; if norm(Y11(i,:)) <= norm(Y22(i,:)) Predict_Y(i,:)=1; else Predict_Y(i,:)=-1; end end