Neural Processing Letters

, Volume 14, Issue 1, pp 49–60

Ensemble of Independent Factor Analyzers with Application to Natural Image Analysis

Authors

  • Akio Utsugi
    • National Institute of Advanced Industrial Science and Technology
Article

DOI: 10.1023/A:1011330208458

Cite this article as:
Utsugi, A. Neural Processing Letters (2001) 14: 49. doi:10.1023/A:1011330208458

Abstract

In this paper the ensemble of independent factor analyzers (EIFA) is proposed. This new statistical model assumes that each data point is generated by the sum of outputs of independently activated factor analyzers. A maximum likelihood (ML) estimation algorithm for the parameter is derived using a Monte Carlo EM algorithm with a Gibbs sampler. The EIFA model is applied to natural image data. With the progress of the learning, the independent factor analyzers develop into feature detectors that resemble complex cells in mammalian visual systems. Although this result is similar to the previous one obtained by independent subspace analysis, we observe the emergence of complex cells from natural images in a more general framework of models, including overcomplete models allowing additive noise in the observables.

complex cellfactor analysisGabor functionindependent component analysisinvariant-feature detectionsparse coding

Copyright information

© Kluwer Academic Publishers 2001