LPP程序.rar_LPP人脸识别_LPP特征提取_LPP算法_matlab 人脸 LPP_人脸识别

function W = constructW(fea,options) % Usage: % W = constructW(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: [Default 'KNN'] % 'KNN' - k = 0 % Complete graph % k > 0 % 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 k=5. % 'Supervised' - k = 0 % Put an edge between two nodes if and % only if they belong to same class. % k > 0 % Put an edge between two nodes if % they belong to same class and they % are among the k nearst neighbors of % each other. % Default: k=0 % You are required to provide the label % information gnd in the options. % % WeightMode - Indicates how to assign weights for each edge % in the graph. Choices are: % 'Binary' - 0-1 weighting. Every edge receiveds weight % of 1. % 'HeatKernel' - If nodes i and j are connected, put weight % W_ij = exp(-norm(x_i - x_j)/2t^2). You are % required to provide the parameter t. [Default One] % 'Cosine' - If nodes i and j are connected, put weight % cosine(x_i,x_j). % % k - The parameter needed under 'KNN' NeighborMode. % Default will be 5. % gnd - The parameter needed under 'Supervised' % NeighborMode. Colunm vector of the label % information for each data point. % bLDA - 0 or 1. Only effective under 'Supervised' % NeighborMode. If 1, the graph will be constructed % to make LPP exactly same as LDA. Default will be % 0. % t - The parameter needed under 'HeatKernel' % WeightMode. Default will be 1 % bNormalized - 0 or 1. Only effective under 'Cosine' WeightMode. % Indicates whether the fea are already be % normalized to 1. Default will be 0 % bSelfConnected - 0 or 1. Indicates whether W(i,i) == 1. Default 0 % if 'Supervised' NeighborMode & bLDA == 1, % bSelfConnected will always be 1. Default 0. % bTrueKNN - 0 or 1. If 1, will construct a truly kNN graph % (Not symmetric!). Default will be 0. Only valid % for 'KNN' NeighborMode % % % Examples: % % fea = rand(50,15); % options = []; % options.NeighborMode = 'KNN'; % options.k = 5; % options.WeightMode = 'HeatKernel'; % options.t = 1; % W = constructW(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.WeightMode = 'HeatKernel'; % options.t = 1; % W = constructW(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.bLDA = 1; % W = constructW(fea,options); % % % For more details about the different ways to construct the W, please % refer: % Deng Cai, Xiaofei He and Jiawei Han, "Document Clustering Using % Locality Preserving Indexing" IEEE TKDE, Dec. 2005. % % % Written by Deng Cai (dengcai2 AT cs.uiuc.edu), April/2004, Feb/2006, % May/2007 % bSpeed = 1; if (~exist('options','var')) options = []; end if isfield(options,'Metric') % warning('This function has been changed and the Metric is no longer be supported'); end if ~isfield(options,'bNormalized') options.bNormalized = 0; end %================================================= if ~isfield(options,'NeighborMode') options.NeighborMode = 'KNN'; end switch lower(options.NeighborMode) case {lower('KNN')} %For simplicity, we include the data point itself in the kNN if ~isfield(options,'k') options.k = 5; end case {lower('Supervised')} if ~isfield(options,'bLDA') options.bLDA = 0; end if options.bLDA options.bSelfConnected = 1; end if ~isfield(options,'k') options.k = 0; end if ~isfield(options,'gnd') error('Label(gnd) should be provided under ''Supervised'' NeighborMode!'); end if ~isempty(fea) && length(options.gnd) ~= size(fea,1) error('gnd doesn''t match with fea!'); end otherwise error('NeighborMode does not exist!'); end %================================================= if ~isfield(options,'WeightMode') options.WeightMode = 'HeatKernel'; end bBinary = 0; bCosine = 0; switch lower(options.WeightMode) case {lower('Binary')} bBinary = 1; case {lower('HeatKernel')} if ~isfield(options,'t') nSmp = size(fea,1); if nSmp > 3000 D = EuDist2(fea(randsample(nSmp,3000),:)); else D = EuDist2(fea); end options.t = mean(mean(D)); end case {lower('Cosine')} bCosine = 1; otherwise error('WeightMode does not exist!'); end %================================================= if ~isfield(options,'bSelfConnected') options.bSelfConnected = 0; end %================================================= if isfield(options,'gnd') nSmp = length(options.gnd); else nSmp = size(fea,1); end maxM = 62500000; %500M BlockSize = floor(maxM/(nSmp*3)); if strcmpi(options.NeighborMode,'Supervised') Label = unique(options.gnd); nLabel = length(Label); if options.bLDA G = zeros(nSmp,nSmp); for idx=1:nLabel classIdx = options.gnd==Label(idx); G(classIdx,classIdx) = 1/sum(classIdx); end W = sparse(G); return; end switch lower(options.WeightMode) case {lower('Binary')} if options.k > 0 G = zeros(nSmp*(options.k+1),3); idNow = 0; for i=1:nLabel classIdx = find(options.gnd==Label(i)); D = EuDist2(fea(classIdx,:),[],0); [dump idx] = sort(D,2); % sort each row clear D dump; idx = idx(:,1:options.k+1); nSmpClass = length(classIdx)*(options.k+1);