@inproceedings{4811b5d3d4714c259389249a9e4854c3,
title = "Projection pursuit fitting Gaussian mixture models",
abstract = "Gaussian mixture models (GMMs) are widely used to model complex distributions. Usually the parameters of the GMMs are determined in a maximum likelihood (ML) framework. A practical deficiency of ML fitting of the GMMs is the poor performance when dealing with high-dimensional data since a large sample size is needed to match the numerical accuracy that is possible in low dimensions. In this paper we propose a method for fitting the GMMs based on the projection pursuit (PP) strategy. By means of simulations we show that the proposed method outperforms ML fitting of the GMMs for small sizes of training sets.",
author = "Mayer Aladjem",
note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2002.; Joint IAPR 9th International Workshop on Structural and Syntactic Pattern Recognition, SSPR 2002 and 4th International Workshop on Statistical Techniques in Pattern Recognition, SPR 2002 ; Conference date: 06-08-2002 Through 09-08-2002",
year = "2002",
month = jan,
day = "1",
doi = "10.1007/3-540-70659-3_41",
language = "English",
isbn = "3540440119",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "396--404",
editor = "Terry Caelli and Adnan Amin and Duin, {Robert P.W.} and {de Ridder}, Dick and Mohamed Kamel",
booktitle = "Structural, Syntactic, and Statistical Pattern Recognition - Joint IAPR International Workshops SSPR 2002 and SPR 2002, Proceedings",
address = "Germany",
}