DMCA
Independent Factor Analysis (1999)
| Venue: | Neural Computation |
| Citations: | 276 - 9 self |
BibTeX
@ARTICLE{Attias99independentfactor,
author = {H. Attias},
title = {Independent Factor Analysis},
journal = {Neural Computation},
year = {1999},
volume = {11},
pages = {803--851}
}
Share
 |
 |
 |
 |
||
OpenURL
Abstract
We introduce the independent factor analysis (IFA) method for recovering independent hidden sources from their observed mixtures. IFA generalizes and unifies ordinary factor analysis (FA), principal component analysis (PCA), and independent component analysis (ICA), and can handle not only square noiseless mixing, but also the general case where the number of mixtures differs from the number of sources and the data are noisy. IFA is a two-step procedure. In the first step, the source densities, mixing matrix and noise covariance are estimated from the observed data by maximum likelihood. For this purpose we present an expectation-maximization (EM) algorithm, which performs unsupervised learning of an associated probabilistic model of the mixing situation. Each source in our model is described by a mixture of Gaussians, thus all the probabilistic calculations can be performed analytically. In the second step, the sources are reconstructed from the observed data by an optimal non-linear ...
Keyphrases
independent factor analysis mixture differs square noiseless mixing general case independent component analysis first step ifa generalizes source density principal component analysis unifies ordinary factor analysis maximum likelihood probabilistic model observed mixture probabilistic calculation two-step procedure independent hidden source mixing situation unsupervised learning noise covariance second step
