An ensemble learning approach to independent component analysis

Author

Choudrey, R. ; Penny, W.D. ; Roberts, S.J.

Author_Institution

Dept. of Eng. Sci., Oxford Univ., UK

Volume

1

fYear

2000

fDate

2000

Firstpage

435

Abstract

Independent Component Analysis (ICA) is an important tool for extracting structure from data. ICA is traditionally performed under a maximum likelihood scheme in a latent variable model and in the absence of noise. Although extensively utilised maximum likelihood estimation has well known drawbacks such as overfitting and sensitivity to local-maxima. We propose a Bayesian learning scheme, Variational Bayes or Ensemble Learning, for both latent variables and parameters in the model

Keywords

array signal processing; data analysis; feature extraction; learning (artificial intelligence); maximum likelihood estimation; neural nets; Bayesian learning scheme; Ensemble Learning; Variational Bayes; blind source separation; ensemble learning approach; feature extraction; independent component analysis; latent variable model; maximum likelihood scheme; neural nets; signal processing; structure from data; Bayesian methods; Blind source separation; Data mining; Feature extraction; Gaussian noise; Independent component analysis; Maximum likelihood estimation; Sensor phenomena and characterization; Signal processing; Source separation;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks for Signal Processing X, 2000. Proceedings of the 2000 IEEE Signal Processing Society Workshop

Conference_Location

Sydney, NSW

ISSN

1089-3555

Print_ISBN

0-7803-6278-0

Type

conf

DOI

10.1109/NNSP.2000.889436

Filename

889436