mediandistance.m

clustering_code ,Clustering Through Ranking On Manifolds Version 0.2 Copyright by Markus Breit

function W=medianDistance(dst,K)

%
% Clustering Through Ranking On Manifolds
% Version 0.2
%
% Copyright by Markus Breitenbach and Gregory Z. Grudic
% This code is for your personal and research use only.
%
% http://www.cs.colorado.edu/~grudic/
% http://ucsu.colorado.edu/~breitenm/
%
% This software is provided "as is," without warranty of any kind, express
% or implied.  In no event shall the authors be held liable
% for any direct, indirect, incidental, special or consequential damages
% arising out of the use of or inability to use this software.
%

if nargin<2    
    K=5; % 5 nearest neighbors
end;

[N D] = size(dst);

% takes a distance matrix, determines \sigma_i and \sigma_j per point and
% outputs W

%X2 = sum(X.^2,1);
%distance = repmat(X2,N,1)+repmat(X2',1,N)-2*X'*X;
[sorted,index] = sort(dst);
neighborhood = index(2:(1+K),:);

% determine all sigma's per point
for i=1:N
    sigma(i) = median( dst(i,neighborhood(:,i)) );
end;

W = zeros(N);
for i=1:N
    for j=1:N
        W(i,j)= -dst(i,j)/(sigma(i)*sigma(j));
    end;
end;

W = exp(W);