Gaussian mixture model (GMM) is one of the important models to approximate probability distributions. There are various methods for Gaussian mixture estimation such as the EM algorithm, sampling method, and the Bayes method. In this paper, we are concerned with the Gaussian mixture estimation problem using the variational Bayes (VB), which is an approximation of the Bayes method. In the VB, it is important to choose its initial values carefully since the objective function of the problem is multimodal. In this paper, we propose a method which employs primitive initial point (PIP) as an initial value of the VB and performs multi-directional search from the PIP. We present the motivation and rationale of our method and demonstrate its effectiveness through numerical experiments using real data sets. © 2009 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Ishikawa, Y., Takeuchi, I., & Nakano, R. (2009). Variational Bayes from the primitive initial point for Gaussian mixture estimation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5863 LNCS, pp. 159–166). https://doi.org/10.1007/978-3-642-10677-4_18
Mendeley helps you to discover research relevant for your work.