Using kernel PCA for Initialisation of Variational Bayesian Nonlinear Blind Source Separation Method
2004
Conference Paper
ei
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.
Author(s): | Honkela, A. and Harmeling, S. and Lundqvist, L. and Valpola, H. |
Book Title: | ICA 2004 |
Journal: | Independent Component Analysis and Blind Signal Separation: Fifth International Conference (ICA 2004) |
Pages: | 790-797 |
Year: | 2004 |
Month: | October |
Day: | 0 |
Editors: | Puntonet, C. G., A. Prieto |
Publisher: | Springer |
Department(s): | Empirical Inference |
Bibtex Type: | Conference Paper (inproceedings) |
DOI: | 10.1007/b100528 |
Event Name: | Fifth International Conference on Independent Component Analysis and Blind Signal Separation |
Event Place: | Granada, Spain |
Address: | Berlin, Germany |
Digital: | 0 |
Language: | en |
Organization: | Max-Planck-Gesellschaft |
School: | Biologische Kybernetik |
BibTex @inproceedings{6353, title = {Using kernel PCA for Initialisation of Variational Bayesian Nonlinear Blind Source Separation Method}, author = {Honkela, A. and Harmeling, S. and Lundqvist, L. and Valpola, H.}, journal = {Independent Component Analysis and Blind Signal Separation: Fifth International Conference (ICA 2004)}, booktitle = {ICA 2004}, pages = {790-797}, editors = {Puntonet, C. G., A. Prieto}, publisher = {Springer}, organization = {Max-Planck-Gesellschaft}, school = {Biologische Kybernetik}, address = {Berlin, Germany}, month = oct, year = {2004}, doi = {10.1007/b100528}, month_numeric = {10} } |