dualmaxsparsevariance2.m.svn-base

a function inside machine learning

function b = dualMaxSparseVariance2(K, Kj, Y, Yj, kernelCols)
%A function to compute the maximum sparse variance vector between matrix
%K and vector y
%We just use the example with the highest absolute variance and select from
%only a few columns of the kernel matrix 

if (nargin ~= 5)
    fprintf('%s\n', help(sprintf('%s', mfilename)));
    error('Incorrect number of inputs - see above usage instructions.');
end

numExamples = size(Kj, 1); 
columnIndices = (1:numExamples)'; 

[variances, bs] = sparseVariances(K, Kj, Y, Yj, columnIndices); 

permutationVector = randperm(numExamples);
colIndices = permutationVector(1:kernelCols);
variances2 = variances(colIndices); 
[maxVariance, i] = max(variances2);

b = zeros(numExamples, 1);
b(colIndices(i)) = bs(colIndices(i));