LDA人脸识别的matlab程序

1、以下是LDA的m文件函数: 你稍稍改改就能用了！fun cti on eigvector, eigvalue, elapse = LDA(g nd,opti on s,data) % LDA: Lin ear Discrim inant An alysis%(aXWXa)/(aXXa+ReguAlpha*l)%SVD%Valid%0.1.%regularizati on%matrix%provided%nFea is%eigvector, eigvalue = LDA(gnd, options, data)In put: data gnd-Data matrix. Each row vecto

2、r of fea is a data point. -Colunm vector of the label in formatio n for each data point.options - Struct value in Matlab. The fields in optionsthat can be set:ReguReguAlpha -ReguTyperegularizerRFisherface1: regularized soluti on, a* = argmax0: solve the sinu larity problem byDefault: 0The regulariza

3、ti on parameter.whe n Regu=1. Default value isRidge: Tikh onovCustom: User provided regularizati onDefault: Ridge(nFea x nF ea) regularizati on matrix which should beif ReguType is Custom.the feature nu mber of data matrix1: Fisherface approachPCARatioPCARatio = n Smp -Default: 0The perce ntage ofco

4、mp onent kept in thestep. The perce ntage is calculated based on the eige nv alue. Default is 1 (100%, all the non-zero eige nv alues will be kept. If PCARatio 1, the PCAwill keep exactlycomp onents (does notexact nu mber of non-zeroOutput:eigvector - Each colu mn is an embedd ing function, for adat

5、a point (row vector) x, y = x*eigvector will be the embedd ing result of x.-The sorted eigvalue of LDA eige n-problem.-Time spe nt on differe nt stepseigvalue elapsefea = ran d(50,70);gnd = on es(10,1);o nes(15,1)*2;o nes(10,1)*3;o nes(15,1)*4; opti ons =;opti on s.Fisherface = 1;eigvector, eigvalue

6、 = LDA(gnd, options, fea);Y = fea*eigvector;%n Class%prin cipal%PCA%step%PCARatio prin ciple%exceed the%comp onen ts).%new%Examples:% See also LPP, con structW, LGE%Refere nee:% P. N. Belhumeur, J. P. Hespanha, and D. J. Kriegman,揈 igenfaces% vs. fisherfaces: recog niti on using class specific lin e

7、ar%projection, ? IEEE Transactions on Pattern Analysis and Machine%Intelligenee, vol. 19, no. 7, pp. 711-720, July 1997.% Deng Cai, Xiaofei He, Yuxiao Hu, Jiawei Han, and Thomas Hua ng,%Lear ning a Spatially Smooth Subspace for Face Recog niti on,CVPR2007%De ng Cai, Xiaofei He, Jiawei Han, SRDA: An

8、Efficie nt Algorithm for%Large Scale Discrim inant An alysis, IEEE Tran sact ions on Kno wledgeand%Data Engin eeri ng, 2007.%version 2.1 -Ju ne/2007%version 2.0 -May/2007%version 1.1 -Feb/2006%version 1.0 -April/2004% Writte n by Deng Cai (de ngcai2 AT )%if exist(data,var)global data;endi

9、f (exist(optio ns,var)optio ns =;endif isfield(options,Regu) | options.RegubPCA = 1;if isfield(optio ns,卩 CARatio)optio ns.PCARatio = 1;endelsebPCA = 0;if isfield(opti on s,ReguType)opti on s.ReguType = Ridge;endif isfield(opti on s,ReguAlpha)opti on s.ReguAlpha = 0.1; endendtmp_T = cputime;% = In i

10、tializati onn Smp ,nF ea = size(data);if len gth(g nd) = n Smperror(g nd and data mismatch!);endclassLabel = uniq ue(g nd);n Class = len gth(classLabel);Dim = n Class - 1;if bPCA & isfield(optio ns,Fisherface) & opti on s.Fisherface opti on s.PCARatio = n Smp - n Class;endif issparse(data)data = ful

11、l(data);endsampleMea n = mean( data,1);data = (data - repmat(sampleMea n,n Smp,1);bChol = 0;if bPCA & (n Smp nFea+1) & (optio ns.PCARatio = 1) DPrime = data*data;DPrime = max(DPrimeQPrime);R,p = chol(DPrime);if p = 0bPCA = 0;bChol = 1;endend%=% SVD%=if bPCAif n Smp nFeaddata = data*data;ddata = max(

12、ddata,ddata);eigvector_PCA, eigvalue_PCA = eig(ddata); eigvalue_PCA = diag(eigvalue_PCA);clear ddata;maxEigValue = max(abs(eigvalue_PCA); eigIdx = fin d(eigvalue_PCA/maxEigValue 1idx = optio ns.PCARatio;if idx le ngth(eigvalue_PCA)eigvalue_PCA = eigvalue_PCA(1:idx);eigvector_PCA = eigvector_PCA(:,1:

13、idx);endelseif optio ns.PCARatio = sumEigbreak;endendeigvalue_PCA = eigvalue_PCA(1:idx);eigvector_PCA = eigvector_PCA(:,1:idx);end %=eigvalue_PCA = eigvalue_PCA.A-.5;data = (data*eigvector_PCA).*repmat(eigvalue_PCA, nSmp,1); elseddata = data*data;ddata = max(ddata,ddata);eigvector, eigvalue_PCA = ei

14、g(ddata); eigvalue_PCA = diag(eigvalue_PCA);clear ddata;maxEigValue = max(eigvalue_PCA);eigIdx = fin d(eigvalue_PCA/maxEigValue 1idx = optio ns.PCARatio;if idx le ngth(eigvalue_PCA)eigvalue_PCA = eigvalue_PCA(1:idx); eigvector = eigvector(:,1:idx);endelseif options.PCARatio = sumEigbreak;endendeigva

15、lue_PCA = eigvalue_PCA(1:idx); eigvector = eigvector(:,1:idx);end%=eigvalue_PCA = eigvalue_PCA.A-.5;eigvector_PCA = (data*eigvector).*repmat(eigvalue_PCA, nF ea,1);data = eigvector;clear eigvector;end elseif bCholDPrime = data*data;%opti on s.ReguAlpha = n Smp*opti on s.ReguAlpha;switch lower(optio

16、ns.ReguType)case lower(Ridge)for i=1:size(DPrime,1)DPrime(i,i) = DPrime(i,i) + opti on s.ReguAlpha; endcase lower(Te nsor)DPrime = DPrime +opti on s.ReguAlpha*opti on s.regularizerR;case lower(Custom)DPrime = DPrime +opti on s.ReguAlpha*opti on s.regularizerR;otherwise error(ReguType does not exist!

17、); endDPrime = max(DPrime,DPrime);endendn Smp ,nF ea = size(data);Hb = zeros( nClass, nF ea);for i = 1:n Class,in dex = fin d(g nd=classLabel(i);classMea n = mea n(data(i ndex,:),1);Hb (i,:) = sqrt(le ngth(i ndex)*classMea n;endelapse.timeW = 0;elapse.timePCA = cputime - tmp_T;tmp_T = cputime;if bPC

18、AdumpVec,eigvalue,eigvector = svd(Hb,ec on);eigvalue = diag(eigvalue); eigldx = fin d(eigvalue dimMatrixDim = dimMatrix;endif isfield(opti on s,bEigs)if opti on s.bEigsbEigs = 1;elsebEigs = 0;endelseif (dimMatrix 1000 & Dim 500 & Dim 250 & Dim dimMatrix/30) bEigs = 1;elsebEigs = 0;endendif bEigs%disp(use eigs to speed up!);opti on = struct(disp,0);