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Abstract: In this paper, we shall focus here on mixtures of factor analyzers from the perspective of a method for model-based density estimation from high-dimensional data, and hence for the clustering of such data. This model enables a normal mixture model to be tted to high-dimensional data. The number of free parameters is controlled through the dimension of the latent factor space. By working in this reduced space it allows an interpolation in model complexities from isotropic to full... (Update)
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BibTeX entry: (Update)
@inproceedings{ achlan00mixtures,
author = "Geoffrey Mc{L}achlan",
title = "Mixtures of Factor Analyzers",
booktitle = "Proc. 17th International Conf. on Machine Learning",
publisher = "Morgan Kaufmann, San Francisco, CA",
pages = "599--606",
year = "2000",
url = "citeseer.ist.psu.edu/306154.html" }
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3 The EM algorithm|an old folk song sung to a fast new tune (context) - Meng, van Dyk - 1997
2 The EM algorithm for factor analyzers (context) - Ghahramani, Hinton - 1997
1 Probabilistic principal components (context) - Tipping, Bishop - 1999
1 Finite mixtures in conrmatory factor-analysis models (context) - Yung - 1997
1 Estimation and tests of signicance in factor analysis (context) - Rao - 1955
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