A new kalman filtering algorithm for nonlinear principal component analysis

Authors:
Xiaolong Zhu;Xianda Zhang;Ying Jia
Affiliations:
Department of Automation, Tsinghua University, Beijing, China;Department of Automation, Tsinghua University, Beijing, China;Intel China Research Center, Beijing, China
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
Year:
2005

Citing 2
Cited 0

Adaptive blind separation with an unknown number of sources

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Projection approximation subspace tracking

IEEE Transactions on Signal Processing

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Abstract

This paper addresses the problem of blind source separation (BSS) based on nonlinear principal component analysis (NPCA), and presents a new Kalman filtering algorithm, which applies a different state-space representation from the one proposed recently by Lv et al. It is shown that the new Kalman filtering algorithm can be simplified greatly under certain conditions, and it includes the existing Kalman-type NPCA algorithm as a special case. Comparisons are made with several related algorithms and computer simulations on BSS are reported to demonstrate the validity.