Self-stabilized minor subspace extraction algorithm based on Householder transformation

Author

Abed-Meraim, K. ; Attallah, S. ; Chkeif, A. ; Hua, Y.

Author_Institution

TSI Dept., Telecom Paris, France

fYear

2000

fDate

2000

Firstpage

90

Lastpage

93

Abstract

In this paper, we propose an orthogonalized version of Oja´s algorithm (OOja) that can be used for the estimation of minor and principal subspaces of a vector sequence. The new algorithm offers, as compared to Oja, such advantages as orthogonality of the weight matrix, which is ensured at each iteration, numerical stability and a quite similar computational complexity

Keywords

computational complexity; iterative methods; matrix algebra; numerical stability; principal component analysis; signal processing; Householder transformation; computational complexity; iteration; minor subspace estimation; numerical stability; orthogonalized Oja´s algorithm; principal subspace estimation; self-stabilized minor subspace extraction algorithm; vector sequence; weight matrix orthogonality; Computational complexity; Covariance matrix; Equations; Error correction; Numerical stability; Principal component analysis; Telecommunications; Wireless communication;

fLanguage

English

Publisher

ieee

Conference_Titel

Statistical Signal and Array Processing, 2000. Proceedings of the Tenth IEEE Workshop on

Conference_Location

Pocono Manor, PA

Print_ISBN

0-7803-5988-7

Type

conf

DOI

10.1109/SSAP.2000.870088

Filename

870088