A self-stabilized minor subspace rule

Scott C. Douglas; S. Y. Kung; Shun Ichi Amari

doi:10.1109/97.735427

A self-stabilized minor subspace rule

Scott C. Douglas, S. Y. Kung, Shun Ichi Amari

Research output: Contribution to journal › Article › peer-review

94 Scopus citations

Abstract

In this letter, we present a minor subspace rule that extracts the subspace that spans the m minor components of a n-dimensional vector stationary random process, m < n. The algorithm is self-stabilizing such that the subspace vectors do not need to be periodically normalized to unit modulus, and the algorithm does not require matrix inversions or divides to maintain its stable behavior.

Original language	English (US)
Pages (from-to)	328-330
Number of pages	3
Journal	IEEE Signal Processing Letters
Volume	5
Issue number	12
DOIs	https://doi.org/10.1109/97.735427
State	Published - Dec 1 1998

All Science Journal Classification (ASJC) codes

Signal Processing
Electrical and Electronic Engineering
Applied Mathematics

Keywords

Adaptive algorithm
Minor component analysis
Subspace methods

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10.1109/97.735427

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