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  Fast Kernel-Based Independent Component Analysis

Shen, H., Jegelka, S., & Gretton, A. (2009). Fast Kernel-Based Independent Component Analysis. IEEE Transactions on Signal Processing, 57(9), 3498-3511. doi:10.1109/TSP.2009.2022857.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-C2E2-A Version Permalink: http://hdl.handle.net/21.11116/0000-0002-BE26-E
Genre: Journal Article

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Shen, H, Author              
Jegelka, S1, 2, Author              
Gretton, A1, 2, Author              
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: Recent approaches to independent component analysis (ICA) have used kernel independence measures to obtain highly accurate solutions, particularly where classical methods experience difficulty (for instance, sources with near-zero kurtosis). FastKICA (fast HSIC-based kernel ICA) is a new optimization method for one such kernel independence measure, the Hilbert-Schmidt Independence Criterion (HSIC). The high computational efficiency of this approach is achieved by combining geometric optimization techniques, specifically an approximate Newton-like method on the orthogonal group, with accurate estimates of the gradient and Hessian based on an incomplete Cholesky decomposition. In contrast to other efficient kernel-based ICA algorithms, FastKICA is applicable to any twice differentiable kernel function. Experimental results for problems with large numbers of sources and observations indicate that FastKICA provides more accurate solutions at a given cost than gradient descent on HSIC. Comparing with other recently published ICA methods, FastKICA is competitive in terms of accuracy, relatively insensitive to local minima when initialized far from independence, and more robust towards outliers. An analysis of the local convergence properties of FastKICA is provided.

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 Dates: 2009-09
 Publication Status: Published in print
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 Rev. Method: -
 Identifiers: DOI: 10.1109/TSP.2009.2022857
BibTex Citekey: 5847
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Title: IEEE Transactions on Signal Processing
  Other : IEEE Trans. Signal Process.
Source Genre: Journal
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Publ. Info: New York, NY : Institute of Electrical and Electronics Engineers
Pages: - Volume / Issue: 57 (9) Sequence Number: - Start / End Page: 3498 - 3511 Identifier: ISSN: 1053-587X
CoNE: https://pure.mpg.de/cone/journals/resource/954925594517