Modeling of Mixtures of Principal Component Analysis Model with Genetic Algorithm

Abstract

Probabilistic principal component analysis is a multivariate Gaussian model where the principal components are used. The mixture of such kernel functions can be a general tool for expressing probability density functions. However, it has not been extensively discussed how to decide the number of kernels nor where to fix the initial points. In this paper we propose to use the genetic algorithm to overcome those problems.