dualpcatrain.m.svn-base

a function inside machine learning

function  [subspaceInfo, trainInfo] = dualPCATrain(trainData, params) 
%Train KPCA

if (nargin ~= 2)
    fprintf('%s\n', help(sprintf('%s', mfilename)));
    error('Incorrect number of inputs - see above usage instructions.');
end

[nameX, nameY] = getSpaceNames(params); 
[numTrainExamples, numFeatures] = getDataFieldSize(trainData, nameX); 

%Store parameters
T = params.iterations; 
kernelFunction = char(params.X.kernel.name); 
kernelParams = params.X.kernel.params; 

trainK = full(feval(kernelFunction, getDataFieldValue(trainData, nameX), getDataFieldValue(trainData, nameX), kernelParams));

T = min(T, rank(trainK)); 
OPTS.disp = 0; 

%Do the training 
tic
[B, D] = eigs(trainK, T, 'LM', OPTS);
trainTime = toc; 

D2 = diag(diag(D).^(-1/2));

trainInfo.data = data; 
trainInfo.data = addDataField(trainInfo.data, 'X', trainK*B*D2, 'examples'); 

subspaceInfo.trainTime = trainTime; 
subspaceInfo.B = B*D2;