Search All of the Math Forum:
Views expressed in these public forums are not endorsed by
Drexel University or The Math Forum.
|
|
Cem DEMiRKIR
Posts:
1
From:
TURKiYE(TURKEY)
Registered:
5/17/06
|
|
Numerical Issues about Computation of log likelihood in High Dimension
Posted:
May 17, 2006 3:20 AM
|
|
Dear Community Members
I'd like to compute likelihood score sum of the clustering made by KMeans algorithm by assuming that each cluster is a gaussian distributed samples. When I compute the likelihood for a sample using the following negatif log-likelihood formula
log-Likelihood = (x-mu)'*SigmaInv*(x-mu) + (d/2)ln(2pi)
+ (1/2)ln |SigmaInv|
where x : sample vector, mu : cluster mean sample vector, d : dimension of the sample vectors, SigmaInv : inverse of the covariance matrix sigma, the determinant of the |SigmaInv| goes beyond the numerical limit of the computer, and gives infinity error since d is very high such as about 400. What can be done to overcome this numerical problem and to compute the likelihoods of the samples ?
Sincerely
Cem DEMiRKIR
|
|
|
|
