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Noisy Independent Factor Analysis Model
for Density Estimation and Classification
U. Amato, A. Antoniadis, A. Samarov and A.B. Tsybakov
(2009)
arxiv paper.
AbstractWe consider the problem of multivariate density estimation when the unknown
density is assumed to follow a particular form of dimensionality reduction, a noisy
independent factor analysis (IFA) model. In this model the data are generated by
a number of latent independent components having unknown distributions and are
observed in Gaussian noise. We do not assume that either the number of components
or the matrix mixing the components are known. We show that the densities of this
form can be estimated with a fast rate. Using the mirror averaging aggregation
algorithm, we construct a density estimator which achieves a nearly parametric
rate, independent of the dimensionality of the data. [Edit]
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