I was inputting a data=11 x 2180 comprises of 11 dimensions with 2180 observations to the function mixGaussFit(data, 2). It prompted the following error. How do i solved "Matrix must be positive definite."
Error using chol
Matrix must be positive definite.
Error in gaussSample (line 20)
A = chol(Sigma, 'lower');
Error in kmeansFit (line 42)
noise = gaussSample(zeros(1, length(v)), 0.01*diag(v), K);
Error in kmeansInitMixGauss (line 7)
[mu, assign] = kmeansFit(data, K);
Error in mixGaussFit>initGauss (line 38)
[mu, Sigma, model.mixWeight] = kmeansInitMixGauss(X, nmix);
Error in mixGaussFit>@(m,X,r)initGauss(m,X,r,initParams,prior) (line 24)
initFn = @(m, X, r)initGauss(m, X, r, initParams, prior);
Error in emAlgo (line 56)
model = init(model, data, restartNum);
Error in mixGaussFit (line 25)
[model, loglikHist] = emAlgo(model, data, initFn, @estep, @mstep , ...
OS: Windows & with Matlab 2015a
I was inputting a data=11 x 2180 comprises of 11 dimensions with 2180 observations to the function mixGaussFit(data, 2). It prompted the following error. How do i solved "Matrix must be positive definite."
Error using chol Matrix must be positive definite. Error in gaussSample (line 20) A = chol(Sigma, 'lower'); Error in kmeansFit (line 42) noise = gaussSample(zeros(1, length(v)), 0.01*diag(v), K); Error in kmeansInitMixGauss (line 7) [mu, assign] = kmeansFit(data, K); Error in mixGaussFit>initGauss (line 38) [mu, Sigma, model.mixWeight] = kmeansInitMixGauss(X, nmix); Error in mixGaussFit>@(m,X,r)initGauss(m,X,r,initParams,prior) (line 24) initFn = @(m, X, r)initGauss(m, X, r, initParams, prior); Error in emAlgo (line 56) model = init(model, data, restartNum); Error in mixGaussFit (line 25) [model, loglikHist] = emAlgo(model, data, initFn, @estep, @mstep , ...