Generalized weighted rules for principal components tracking

Authors:
T. Tanaka
Affiliations:
Lab. for Adv. Brain Signal Process., Brain Sci. Inst., Saitama, Japan
Venue:
IEEE Transactions on Signal Processing
Year:
2005

Citing 0
Cited 4

An Efficient Measure of Signal Temporal Predictability for Blind Source Separation

Neural Processing Letters
Modified Modulated Hebb-Oja Learning Rule: A Method for Biologically Plausible Principal Component Analysis

Neural Information Processing
A novel adaptive eigendecomposition technique with application to automatic target recognition

ICCOMP'06 Proceedings of the 10th WSEAS international conference on Computers
On-line adaptive principal component extraction algorithms using iteration approach

Signal Processing

Quantified Score

Hi-index	35.68

Visualization

Abstract

We investigate a general class of weighted subspace (WS) rules for principal component analysis (PCA) in order to show the difference of the existing rules. We focus in this paper on the well-known weighted principal components tracking rules that are developed by Oja and Xu. We unify these rules to more generalized form that is parameterized by a scalar. It is then proved that the generalized rules are stable at only the fixed point from which the principal components are extracted. We moreover find the parameter of the rules that gives the dynamics preserving orthogonality of estimated principal components most strongly during the tracking. Finally, toy examples and application in adaptive image compression are illustrated to understand the theoretical analysis of the stability.