SNF.m

function [W]=SNF(Wall,K,t,ALPHA)

if nargin < 2
    K = 20;
end
if nargin < 3
    t = 20;
end

if nargin < 4
    ALPHA = 1;
end


C = length(Wall); %number of data types : 
[m,n]=size(Wall{1});

%Construct the normalized similarity matrix P
for i = 1 : C
     Wall{i} = Wall{i}./repmat(sum(Wall{i},2),1,n);
     %  Wall{i} = Wall{i}./min(min(Wall{i}(Wall{i}>0)));   
     Wall{i} = (Wall{i} + Wall{i}')/2;
   
end

%Construct the normalized local similarity matrix S
for i = 1 : C
    newW{i} = FindDominateSet(Wall{i},round(K));
end


Wsum = zeros(m,n);
for i = 1 : C
    Wsum = Wsum + Wall{i}; 
end

for ITER=1:t
    for i = 1 : C
       
        Wall0{i}=newW{i}*(Wsum - Wall{i})*newW{i}'/(C-1); 
    end
    for i = 1 : C
        Wall{i} = BOnormalized(Wall0{i},ALPHA);
    end
    Wsum = zeros(m,n);
    for i = 1 : C
        Wsum = Wsum + Wall{i};
    end

end

W = Wsum/C;  %Pc=(P1+P2+....+Pn)/n
W = W./repmat(sum(W,2),1,n); %normalization of Pc
W = (W +W'+eye(n))/2;
end

function W = BOnormalized(W,ALPHA)
if nargin < 2
    ALPHA = 1;
end
W = W+ALPHA*eye(length(W));
W = (W +W')/2; 
end

function newW = FindDominateSet(W,K) 
[m,n]=size(W);
[YW,IW1] = sort(W,2,'descend'); 
clear YW; 
newW=zeros(m,n);
temp=repmat((1:n)',1,K);
I1=(IW1(:,1:K)-1)*m+temp;
newW(I1(:))=W(I1(:));
newW=newW./repmat(sum(newW,2),1,n);
clear IW1;
clear IW2;
clear temp;
end