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.
This work was supported in part by the Paul Ivanier Center for Robotics and Production Management, Ben-Gurion University of the Negev, Israel.
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Keywords
- Bayesian Information Criterion
- Gaussian Mixture Model
- Latent Variable Model
- Training Sample Size
- Gaussian Mixture Model Model
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Aladjem, M. (2002). Projection Pursuit Fitting Gaussian Mixture Models. In: Caelli, T., Amin, A., Duin, R.P.W., de Ridder, D., Kamel, M. (eds) Structural, Syntactic, and Statistical Pattern Recognition. SSPR /SPR 2002. Lecture Notes in Computer Science, vol 2396. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-70659-3_41
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DOI: https://doi.org/10.1007/3-540-70659-3_41
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