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  Using kernel PCA for Initialisation of Variational Bayesian Nonlinear Blind Source Separation Method

Honkela, A., Harmeling, S., Lundqvist, L., & Valpola, H. (2004). Using kernel PCA for Initialisation of Variational Bayesian Nonlinear Blind Source Separation Method. In C. Puntonet, & A. Prieto (Eds.), Independent Component Analysis and Blind Signal Separation: Fifth International Conference, ICA 2004, Granada, Spain, September 22-24, 2004 (pp. 790-797). Berlin, Germany: Springer.

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 Creators:
Honkela, A, Author
Harmeling, S1, Author           
Lundqvist, L, Author
Valpola, H, Author
Affiliations:
1External Organizations, ou_persistent22              

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 Abstract: The variational Bayesian nonlinear blind source separation method introduced by Lappalainen and Honkela in 2000 is initialised with linear principal component analysis (PCA). Because of the multilayer perceptron (MLP) network used to model the nonlinearity, the method is susceptible to local minima and therefore sensitive to the initialisation used. As the method is used for nonlinear separation, the linear initialisation may in some cases lead it astray. In this paper we study the use of kernel PCA (KPCA) in the initialisation. KPCA is a rather straightforward generalisation of linear PCA and it is much faster to compute than the variational Bayesian method. The experiments show that it can produce significantly better initialisations than linear PCA. Additionally, the model comparison methods provided by the variational Bayesian framework can be easily applied to compare different kernels.

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 Dates: 2004-10
 Publication Status: Issued
 Pages: -
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 Rev. Type: -
 Identifiers: DOI: 10.1007/b100528
BibTex Citekey: 6353
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Title: Fifth International Conference on Independent Component Analysis and Blind Signal Separation (ICA 2004)
Place of Event: Granada, Spain
Start-/End Date: 2004-09-22 - 2004-09-24

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Title: Independent Component Analysis and Blind Signal Separation: Fifth International Conference, ICA 2004, Granada, Spain, September 22-24, 2004
Source Genre: Proceedings
 Creator(s):
Puntonet, CG, Editor
Prieto, A, Editor
Affiliations:
-
Publ. Info: Berlin, Germany : Springer
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 790 - 797 Identifier: ISBN: 978-3-540-23056-4

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Title: Lecture Notes in Computer Science
Source Genre: Series
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Pages: - Volume / Issue: 3195 Sequence Number: - Start / End Page: - Identifier: -