sparsevariances.m

a function inside machine learning

function [variances, bs] = sparseVariances(K, Kj, Y, Yj, columnIndices)
%Comnpute the variances of the columns of the kernel matrix.  ColumnIndices should be a column
%vector. The variable called bs is the values of the selected b's , scaled
%so that b'Kb = 1 (note, we don't scale by kj)

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

numExamples = size(K, 1); 
numColumns = size(K, 2); 

normVector = K(sub2ind(size(K), columnIndices , (1:numColumns)')); 
infiniteIndices = setdiff((normVector == 0) .* (1:numColumns)', 0);

warning('off','MATLAB:divideByZero');
invNormVector = 1./normVector;  
invNormVector(infiniteIndices) = 0;
%invNormVector = 1./sqrt(diag(K(columnIndices, :)));  
warning('on','MATLAB:divideByZero');

variances = sum(Kj.^2, 1)' .* invNormVector; 
bs = sqrt(invNormVector);