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注册: 2008-10-03
专业: 计算机应用技术

[交流] 【讨论】matlab的busy问题

function [Y] = lle(X,K,d)

[D,N] = size(X);
fprintf(1,'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');

if(K>D)
  fprintf(1,' [note: K>D; regularization will be used]\n');
  tol=1e-3; % regularlizer in case constrained fits are ill conditioned
else
  tol=0;
end

W = zeros(K,N);
for ii=1:N
z = X(:,neighborhood(:,ii))-repmat(X(:,ii),1,K); % shift ith pt to origin
C = z'*z;                                     % local covariance
C = C + eye(K,K)*tol*trace(C);                % regularlization (K>D)
W(:,ii) = C\ones(K,1);                         % solve Cw=1
W(:,ii) = W(:,ii)/sum(W(:,ii));                % enforce sum(w)=1
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(:,2:d+1)'*sqrt(N); % bottom evect is [1,1,1,1...] with eval 0

fprintf(1,'Done.\n');

这是我从网上下的程序，大概意思是：输入是一个D×N的矩阵，

把每一列看作一个D维的点，然后找每个点的K个近邻点，并建立

起每个点与他的K个近邻点的关系矩阵，最后的输出是一个d×N

的矩阵（d
一个40×100的矩阵，然后运行后得到了20×100的矩阵，也就是

降了20维，但再拿另一个40×100的矩阵来运行，matlab就busy

了，死机，有如下警告：Matrix is close to singular or badly scaled.
Results may be inaccurate. RCOND = 1.077831e-016.
请各位高手出手相救，不胜感激！

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