Correction to Efficient Computation of Normalized Maximum Likelihood Codes for Gaussian Mixture Models With Its Applications to Clustering [Nov 13 7718-7727]
So Hirai, Kenji Yamanishi · IEEE Transactions on Information Theory · 2019
This paper corrects errors in the calculation of the normalized maximum likelihood (NML) code length for a Gaussian mixture model (GMM). It shows that the NML code length calculated in “efficient computation of the NML codes for the GMMs with its applications to clustering” is an upper bound on the NML code length strictly calculated for the GMM. In addition, we correct the NML code length for generalized logistic distribution.