Gabor小波+LBP特征提取+PCA+LPP降维_人脸识别_matlab

function [W, elapse] = 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: % Metric - Choices are: % 'Euclidean' - Will use the Euclidean distance of two data % points to evaluate the "closeness" between % them. [Default One] % 'Cosine' - Will use the cosine value of two vectors % to evaluate the "closeness" between them. % A popular similarity measure used in % Information Retrieval. % % 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. [Default One] % 'HeatKernel' - If nodes i and j are connected, put weight % W_ij = exp(-norm(x_i - x_j)/2t^2). This % weight mode can only be used under % 'Euclidean' metric and you are required to % provide the parameter t. % 'Cosine' - If nodes i and j are connected, put weight % cosine(x_i,x_j). Can only be used under % 'Cosine' metric. % % 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' metric. % 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 1 % if 'Supervised' NeighborMode & bLDA == 1, % bSelfConnected will always be 1. Default 1. % % % Examples: % % fea = rand(50,15); % options = []; % options.Metric = 'Euclidean'; % 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.Metric = 'Euclidean'; % 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.Metric = 'Euclidean'; % 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 % if (~exist('options','var')) options = []; else if ~isstruct(options) error('parameter error!'); end end %================================================= if ~isfield(options,'Metric') options.Metric = 'Cosine'; end switch lower(options.Metric) case {lower('Euclidean')} case {lower('Cosine')} if ~isfield(options,'bNormalized') options.bNormalized = 0; end otherwise error('Metric does not exist!'); 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 = 'Binary'; end bBinary = 0; switch lower(options.WeightMode) case {lower('Binary')} bBinary = 1; case {lower('HeatKernel')} if ~strcmpi(options.Metric,'Euclidean') warning('''HeatKernel'' WeightMode should be used under ''Euclidean'' Metric!'); options.Metric = 'Euclidean'; end if ~isfield(options,'t') options.t = 1; end case {lower('Cosine')} if ~strcmpi(options.Metric,'Cosine') warning('''Cosine'' WeightMode should be used under ''Cosine'' Metric!'); options.Metric = 'Cosine'; end if ~isfield(options,'bNormalized') options.bNormalized = 0; end otherwise error('WeightMode does not exist!'