Adaptive Mixtures of Factor Analyzers

Heysem Kaya; Albert Ali Salah

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arXiv · 1507.02801

Adaptive Mixtures of Factor Analyzers

Heysem Kaya·Albert Ali Salah

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Abstract

A mixture of factor analyzers is a semi-parametric density estimator that generalizes the well-known mixtures of Gaussians model by allowing each Gaussian in the mixture to be represented in a different lower-dimensional manifold. This paper presents a robust and parsimonious model selection algorithm for training a mixture of factor analyzers, carrying out simultaneous clustering and locally linear, globally nonlinear dimensionality reduction. Permitting different number of factors per mixture component, the algorithm adapts the model complexity to the data complexity. We compare the proposed algorithm with related automatic model selection algorithms on a number of benchmarks. The results indicate the effectiveness of this fast and robust approach in clustering, manifold learning and class-conditional modeling.

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BibTeX RIS

Heysem Kaya, Albert Ali Salah. 2015-07-10. Adaptive Mixtures of Factor Analyzers. https://arxiv.org/abs/1507.02801

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