Adaptive Mixtures of Factor Analyzers
This is an incremental improvement for researchers in machine learning, offering a more robust and parsimonious approach to semi-parametric density estimation.
The paper tackles the problem of model selection for mixtures of factor analyzers by proposing an algorithm that adapts model complexity to data, enabling simultaneous clustering and dimensionality reduction. Results show it is effective and fast on benchmarks.
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.