ICA with sparse connections: Revisited

Kun Zhang*, Heng PENG, Laiwan Chan, Aapo Hyvarinen

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

12 Citations (Scopus)

Abstract

When applying independent component analysis (ICA), sometimes we expect the connections between the observed mixtures and the recovered independent components (or the original sources) to be sparse, to make the interpretation easier or to reduce the random effect in the results. In this paper we propose two methods to tackle this problem. One is based on adaptive Lasso, which exploits the L1 penalty with data-adaptive weights. We show the relationship between this method and the classic information criteria such as BIC and AIC. The other is based on optimal brain surgeon, and we show how its stopping criterion is related to the information criteria. This method produces the solution path of the transformation matrix, with different number of zero entries. These methods involve low computational loads. Moreover, in each method, the parameter controlling the sparsity level of the transformation matrix has clear interpretations. By setting such parameters to certain values, the results of the proposed methods are consistent with those produced by classic information criteria.

Original languageEnglish
Pages (from-to)195-202
Number of pages8
JournalLecture Notes in Computer Science
Volume5441
DOIs
Publication statusPublished - 2009
Event8th International Conference on Independent Component Analysis and Signal Separation, ICA 2009 - Paraty, Brazil
Duration: 15 Mar 200918 Mar 2009

Scopus Subject Areas

  • Theoretical Computer Science
  • Computer Science(all)

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