LDA人脸识别地matlab程序

1、实用文案LDA人脸识别的matlab 程序以下是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%eigvector, eigvalue = LDA(g nd, opti ons, data)标准文档% point.%each%In put:data - Data matrix. Each row vector of fea is a datagnd - Colu nm vector of the

2、label in formatio n fordata point.options - Struct value in Matlab. The fields in optionsthat can be set:Regu -1: regularized soluti on,a* = argmax(aXWXa)/(aXXa+ReguAlpha*l)%0: solve the sinu larity problemby SVD%Default: 0%ReguAlpha -The regularizati on parameter.Valid%whe n Regu=1. Default valueis

3、 0.1.%ReguType - Ridge: Tikh onovregularizati on%Custom: User provided%regularizati onmatrix%Default: Ridge%regularizerR -(nFea x nF ea)regularizati on%matrix which should beprovided%if ReguType is Custom.nFea is%the feature nu mber ofdata%matrix%Fisherface-1: Fisherface approach%PCARatio = n Smp -n

4、 Class%Default: 0%PCARatio- The perce ntage ofprin cipal%comp onent kept in thePCA%step. 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 PCAstep%will keep exactlyPCARatio prin ciple%comp onents (does notexce

5、ed the%exact nu mber ofnon-zero comp onen ts).%Output:%eigvector -Each colu mn is an embedd ing function, fora new%data point (row vector) x,y = x*eigvector%will be the embedd ing result of x.%eigvalue-The sorted eigvalue of LDA eige n-problem.%elapse-Time spe nt on differe nt steps%Examples:%fea =

6、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=LDA(gnd, options, fea);%Y = fea*eigvector;% See also LPP, con structW, LGE%Refere nee:% P. N. Belhumeur, J. P. Hespanha, and D. J. Kriegman, 揈 igenfaces%vs. fisherfaces

7、: recog niti on using class specific lin ear% 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%D

8、e ng Cai, Xiaofei He, Jiawei Han, SRDA: An Efficie nt Algorithm for%Large Scale Discrim inant An alysis, IEEE Tran sact ions on Kno wledgeand% Data Engin eeri ng, 2007.%实用文案%version2.1-Ju ne/2007%version2.0-May/2007%version1.1-Feb/2006%version1.0-April/2004%Writte n by Deng Cai (de ngcai2 AT cs.uiuc

9、.edu)%if exist(data,var) global data;endif (exist(optio ns,var) optio ns =;endif isfield(options,Regu) | options.Regu bPCA = 1;if isfield(optio ns,卩 CARatio)optio ns.PCARatio = 1;elseend标准文档实用文案bPCA = 0;if isfield(opti on s,ReguType)opti on s.ReguType = Ridge;endif isfield(opti on s,ReguAlpha)opti o

10、n s.ReguAlpha = 0.1;endendtmp_T = cputime;% = In itializati 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.Fisherfaceopti on s.PCARatio

11、 = n Smp - n Class;endif issparse(data)data = full(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;end%=% SVD%=if bP

12、CAif n Smp nFeaddata = data*data;ddata = max(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_P

13、CA(1:idx); eigvector_PCA = eigvector_PCA(:,1:idx); endelseif options.PCARatio = sumEig break;endendeigvalue_PCA = eigvalue_PCA(1:idx); eigvector_PCA = eigvector_PCA(:,1:idx);%= eigvalue_PCA = eigvalue_PCA4-.5;elsedata = (data*eigvector_PCA).*repmat(eigvalue_PCA, nSmp,1); ddata = data*data;ddata = ma

14、x(ddata,ddata);eigvector, eigvalue_PCA = eig(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

15、options.PCARatio = sumEig break;endendeigvalue_PCA = eigvalue_PCA(1:idx); eigvector = eigvector(:,1:idx);end%= eigvalue_PCA = eigvalue_PCA4-.5;eigvector_PCA =(data*eigvector).*repmat(eigvalue_PCA, nF ea,1);data = eigvector;clear eigvector;endelseif bCholDPrime = data*data;%opti on s.ReguAlpha = n Sm

16、p*opti on s.ReguAlpha;switch lower(optio 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;

17、otherwiseerror(ReguType does not exist!); 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 =

18、 cputime - tmp_T;tmp_T = cputime;if bPCAdumpVec,eigvalue,eigvector = svd(Hb,ec on);eigvalue = diag(eigvalue);eigIdx = 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 = struc