% LLE ALGORITHM (using K nearest neighbors) with Low-Dimensional % Neighborhood Representation % % [Y] = lle_ldnr(X,K,dmax) % % X = data as D x N matrix (D = dimensionality, N = #points) % K = number of neighbors % dmax = max embedding dimensionality % Y = embedding as dmax x N matrix % % This is a modification of the original LLE code as appears in LLE's % homepage http://www.cs.toronto.edu/~roweis/lle/code/lle.m % % The computation of the weight vectors in this version is based on first % projection the local neighborhood and then find the best weight vector % with respecet to the projected neighborhood. % (see LLE with low-dimensional neighborhood representation, % http://arxiv.org/abs/0808.0780 ) % % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% function [Y] = lle_ldnr(X,K,d) [D,N] = size(X); fprintf(1,'Local PCA LLE running on %d points in %d dimensions\n',N,D); % STEP1: COMPUTE PAIRWISE DISTANCES & FIND NEIGHBORS fprintf(1,'-->Finding %d nearest neighbours.\n',K); X2 = sum(X.^2,1); distance = repmat(X2,N,1)+repmat(X2',1,N)-2*X'*X; [sorted,index] = sort(distance); neighborhood = index(2:(1+K),:); % STEP2: SOLVE FOR RECONSTRUCTION WEIGHTS fprintf(1,'-->Solving for reconstruction weights.\n'); W = zeros(K,N); for ii=1:N z = X(:,neighborhood(:,ii))'-repmat(X(:,ii),1,K)'; % shift ith pt to origin [U,L,V]=svd(z); U=U(:,d+1:K); a=U'*ones(K,1); a=a/(a'*a); W(:,ii) =U*a; end; % STEP 3: COMPUTE EMBEDDING FROM EIGENVECTS OF COST MATRIX M=(I-W)'(I-W) fprintf(1,'-->Computing embedding.\n'); % M=eye(N,N); % use a sparse matrix with storage for 4KN nonzero elements M = sparse(1:N,1:N,ones(1,N),N,N,4*K*N); for ii=1:N w = W(:,ii); jj = neighborhood(:,ii); M(ii,jj) = M(ii,jj) - w'; M(jj,ii) = M(jj,ii) - w; M(jj,jj) = M(jj,jj) + w*w'; end; % CALCULATION OF EMBEDDING options.disp = 0; options.isreal = 1; options.issym = 1; [Y,eigenvals] = eigs(M,d+1,0,options); Y = Y(:,1:d)'*sqrt(N); % bottom evect is [1,1,1,1...] with eval 0 fprintf(1,'Done.\n');