PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Noisy Independent Factor Analysis Model for Density Estimation and Classification
U. Amato, A. Antoniadis, A. Samarov and A.B. Tsybakov
(2009) arxiv paper.

Abstract

We 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.

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Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
ID Code:6923
Deposited By:Alexandre Tsybakov
Deposited On:16 April 2010