initialization4LPKSVD.m

% ========================================================================
% Initialization for Locality preserving KSVD algorithm
% USAGE: [Dinit,Tinit,Winit,Q] = initialization4LPKSVD(training_feats,....
%                       H_train,dictsize,iterations,sparsitythres,Q_in,num)
% Inputs
%       training_feats  -training features
%       H_train         -label matrix for training feature 
%       dictsize        -number of dictionary items
%       iterations      -iterations
%       sparsitythres   -sparsity threshold
%       Q_in            -initialized locality preserving matrix
%       num             -iteration number
% Outputs
%       Dinit           -initialized dictionary
%       Tinit           -initialized linear transform matrix
%       Winit           -initialized classifier parameters
%       Q               -computed locality preserving matrix 
%   
% Author: Weiyang Liu (wyliu@pku.edu.cn)
% Date: 11-20-2014
% ========================================================================


function [Dinit,Tinit,Winit,Q]=initialization4LPKSVD(training_feats,H_train,dictsize,iterations,sparsitythres,Q_in,num)

numClass = size(H_train,1); % number of objects
numPerClass = round(dictsize/numClass); % initial points from each classes
Dinit = []; 
dictLabel = [];
for classid=1:numClass
    col_ids = find(H_train(classid,:)==1);
    data_ids = find(colnorms_squared_new(training_feats(:,col_ids)) > 1e-6);   % ensure no zero data elements are chosen
    %    perm = randperm(length(data_ids));
    perm = [1:length(data_ids)]; 
    %%%  Initilization for LP-KSVD (perform KSVD in each class)
    Dpart = training_feats(:,col_ids(data_ids(perm(1:numPerClass))));
    para.data = training_feats(:,col_ids(data_ids));
    para.Tdata = sparsitythres;
    para.iternum = iterations;
    para.memusage = 'high';
    % normalization
    para.initdict = normcols(Dpart);
    % ksvd process
    [Dpart,Xpart,Errpart] = ksvd(para,'');
    Dinit = [Dinit Dpart];
    
    labelvector = zeros(numClass,1);
    labelvector(classid) = 1;
    dictLabel = [dictLabel repmat(labelvector,1,numPerClass)];
end

% Q (label-constraints code); T: scale factor
T = eye(dictsize,dictsize); % scale factor
if num==1
% Q=Qinput;
Q = zeros(dictsize,size(training_feats,2)); % energy matrix
else
% for frameid=1:size(training_feats,2)
%     [dists,neighbors] = top_K_neighbors( training_feats,training_feats(:,frameid),10 );
%     Q(neighbors,frameid)=1;
% end
Q=Q_in;
end

params.data = training_feats;
params.Tdata = sparsitythres; % spasity term
params.iternum = iterations;
params.memusage = 'high';

% normalization
params.initdict = normcols(Dinit);

% ksvd process
[Dtemp,Xtemp,Errtemp] = ksvd(params,'');

% learning linear classifier parameters
Winit = inv(Xtemp*Xtemp'+eye(size(Xtemp*Xtemp')))*Xtemp*H_train';
Winit = Winit';

Tinit = inv(Xtemp*Xtemp'+eye(size(Xtemp*Xtemp')))*Xtemp*Q';
Tinit = Tinit';