NPE.rar_NPE人脸识别_人脸 数据集_流型_流型人脸识别_流型识别

function M = constructM(fea,options) % Usage: % M = constructM(fea,options) % % fea: Rows of vectors of data points. Each row is x_i % options: Struct value in Matlab. The fields in options that can be set: % % NeighborMode - Indicates how to construct the graph. Choices % are: % 'KNN' - Put an edge between two nodes if and % only if they are among the k nearst % neighbors of each other. You are % required to provide the parameter k in % the options. [Default One] % 'Supervised' - Two variations: % 1. k=0, Put an edge between two nodes % if and only if they belong to same class. % 2. k>0, The distance between two nodes % in the same class will be smaller than % two nodes have diff. labels % You are required to provide the label % information gnd in the options. % % k - The number of neighbors. % gnd - The parameter needed under 'Supervised' % NeighborMode. Colunm vector of the label % information for each data point. % % Examples: % % fea = rand(50,15); % options = []; % options.NeighborMode = 'KNN'; % options.k = 5; % M = constructM(fea,options); % % % fea = rand(50,15); % gnd = [ones(10,1);ones(15,1)*2;ones(10,1)*3;ones(15,1)*4]; % options = []; % options.NeighborMode = 'Supervised'; % options.gnd = gnd; % options.k = 0; % M = constructM(fea,options); % % fea = rand(50,15); % gnd = [ones(10,1);ones(15,1)*2;ones(10,1)*3;ones(15,1)*4]; % options = []; % options.NeighborMode = 'Supervised'; % options.gnd = gnd; % options.k = 4; % M = constructM(fea,options); % %Reference: % % Xiaofei He, Deng Cai, Shuicheng Yan, and Hong-Jiang % Zhang, "Neighborhood Preserving Embedding", Tenth IEEE International % Conference on Computer Vision (ICCV'2005), 2005 % % Sam Roweis & Lawrence Saul. "Nonlinear dimensionality reduction by % locally linear embedding", Science, v.290 no.5500 , Dec.22, 2000. % pp.2323--2326. % % http://www.cs.toronto.edu/~roweis/lle/code.html % % % Written by Deng Cai (dengcai@gmail.com), Jan/2005, Jan/2007 % [nSmp, nFea] = size(fea); switch lower(options.NeighborMode) case {lower('KNN')} aa = sum(fea.*fea,2); ab = fea*fea'; Distance = repmat(aa, 1, nSmp) + repmat(aa', nSmp, 1) - 2*ab; Distance = max(Distance,Distance'); Distance = Distance - diag(diag(Distance)); if options.k <= 0 error('k must be greater than 0!'); end case {lower('Supervised')} if options.k > 0 aa = sum(fea.*fea,2); ab = fea*fea'; Distance = repmat(aa, 1, nSmp) + repmat(aa', nSmp, 1) - 2*ab; Distance = max(Distance,Distance'); Distance = Distance - diag(diag(Distance)); WLDA = ones(nSmp,nSmp); Label = unique(options.gnd); nLabel = length(Label); for idx=1:nLabel classIdx = find(options.gnd==Label(idx)); WLDA(classIdx,classIdx) = 0; end Distance = Distance+WLDA*(max(max(Distance))+10); end otherwise error('NeighborMode does not exist!'); end if options.k == 0 W = zeros(nSmp,nSmp); for ii=1:nSmp idx = find(options.gnd==options.gnd(ii)); idx(find(idx==ii)) = []; z = fea(idx,:)-repmat(fea(ii,:),length(idx),1); % shift ith pt to origin C = z*z'; % local covariance tW = C\ones(length(idx),1); % solve Cw=1 tW = tW/sum(tW); % enforce sum(w)=1 W(idx,ii) = tW; end M = (eye(size(W)) - W); M = M*M'; M = max(M,M'); M = sparse(M); else [sorted,index] = sort(Distance,2); neighborhood = index(:,2:(1+options.k)); W = zeros(options.k,nSmp); for ii=1:nSmp z = fea(neighborhood(ii,:),:)-repmat(fea(ii,:),options.k,1); % shift ith pt to origin C = z*z'; % local covariance W(:,ii) = C\ones(options.k,1); % solve Cw=1 W(:,ii) = W(:,ii)/sum(W(:,ii)); % enforce sum(w)=1 end M = sparse(1:nSmp,1:nSmp,ones(1,nSmp),nSmp,nSmp,4*options.k*nSmp); for ii=1:nSmp w = W(:,ii); jj = neighborhood(ii,:)'; M(ii,jj) = M(ii,jj) - w'; M(jj,ii) = M(jj,ii) - w; M(jj,jj) = M(jj,jj) + w*w'; end M = max(M,M'); M = sparse(M); end