Speeding Up VBGMM By Using Logsumexp With the Approximate Exp-function

摘要

Gaussian Mixture Models (GMM) are a representative method to realize clustering and are used in many applications such as probability density modeling and soft clustering. There are several methods for parameter estimation of the GMM, among which the Variational Bayesian Gaussian Mixture Model (VBGMM) is known that they are hard to overfitting. In the VBGMM, the logsumexp takes up most of the computation time. This is due to the heavy use of the exponential function, which is computationally intensive. In this paper, we explain a method to speed up logsumexp by using an approximate exponential function. As a result, logsumexp is accelerated 1.991 times faster, and VBGMM is accelerated 1.229 times faster.