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LSPTSVM

A Matlab code for least squares 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, Nai-Yang Deng*, Zhi-Min Yang. Least squares recursive projection twin support vector machine for classification[J]. Pattern Recognition, 2012, 45(6): 2299-2307.

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: 203–210.


Main Function
function Predict_Y = LSPTSVM(TestX,DataTrain,FunPara) %%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % LSPTSVM: Least squares recursive projection twin support vector machine % % Predict_Y = LSPTSVM(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. % c3: [0,inf] Paramter to tune the weight. % c4: [0,inf] Paramter to tune the weight. % % 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.c3=0.1; % FunPara.c4=0.1; % Predict_Y = LSPTSVM(TestX,DataTrain,FunPara); % % Reference: % Y.-H. Shao, N.-Y. Deng, Z.-M. Yang.Least squares recursive projection % twin support vector machine for classification .Pattern Recognition,2012, % 45(6): 2299-2307. % % Version 1.0 --Apr/2011 % Written by Yuan-Hai Shao (shaoyuanhai21@163.com) %%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Initailization %%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %tic; inputA = DataTrain.A; inputB = DataTrain.B; c1 = FunPara.c1; c2 = FunPara.c2; c3 = FunPara.c3; c4 = FunPara.c4; [m1,n1]=size(inputA); m2=size(inputB,1); e1=ones(m1,1); e2=ones(m2,1); %%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Compute w1 and w2 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%% center1=1/m1*sum(inputA(:,:)); center2=1/m2*sum(inputB(:,:)); S1=(inputA(1,:)-center1)'*(inputA(1,:)-center1); S2=(inputB(1,:)-center2)'*(inputB(1,:)-center2); for i=2:m1 S1=S1+(inputA(i,:)-center1)'*(inputA(i,:)-center1); end for i=2:m2 S2=S2+(inputB(i,:)-center2)'*(inputB(i,:)-center2); end w1=(S1/c1+(inputB-1/m1*e2*e1'*inputA)'*(inputB-1/m1*e2*e1'*inputA)+c3/c1*eye(n1,n1))\((inputB-1/m1*e2*e1'*inputA)'*e2); w2=-(S2/c2+(inputA-1/m2*e1*e2'*inputB)'*(inputA-1/m2*e1*e2'*inputB)+c4/c2*eye(n1,n1))\((inputA-1/m2*e1*e2'*inputB)'*e1); W1=w1; W2=w2; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %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 % center1=1/m1*sum(inputA(:,:)); % center2=1/m2*sum(inputB(:,:)); % S1=(inputA(1,:)-center1)'*(inputA(1,:)-center1); % S2=(inputB(1,:)-center2)'*(inputB(1,:)-center2); % for i=2:m1 % S1=S1+(inputA(i,:)-center1)'*(inputA(i,:)-center1); % end % for i=2:m2 % S2=S2+(inputB(i,:)-center2)'*(inputB(i,:)-center2); % end % S1=S1+eps*eye(n1,n1); % S2=S2+eps*eye(n1,n1); % w1=(S1/c1+(inputB-1/m1*e2*e1'*inputA)'*(inputB-1/m1*e2*e1'*inputA)+c3/c1*eye(n1,n1))\((inputB-1/m1*e2*e1'*inputA)'*e2); % w2=-(S2/c2+(inputA-1/m2*e1*e2'*inputB)'*(inputA-1/m2*e1*e2'*inputB)+c4/c2*eye(n1,n1))\((inputA-1/m2*e1*e2'*inputB)'*e1); % W1=[W1,w1]; % W2=[W2,w2]; % mop=mop-1; % end % toc %%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Predict and output %%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 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
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