An ensemble learning approach to independent component analysis

被引：0

作者：

Choudrey, R ^{[1
]}

Penny, WD ^{[1
]}

Roberts, SJ ^{[1
]}

机构：

[1] Univ Oxford, Dept Engn Sci, Oxford OX1 2JD, England

来源：

NEURAL NETWORKS FOR SIGNAL PROCESSING X, VOLS 1 AND 2, PROCEEDINGS | 2000年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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. In this paper, we propose a Bayesian learning scheme, Variational Bayes or Ensemble Learning, for both latent variables and parameters in the model.

引用

页码：435 / 444

页数：10

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