Asymptotic performance analysis of PCA algorithms based on the weighted subspace criterion

Author

Delmas, Jean Pierre ; Gabillon, Victor

Author_Institution

Dept. CITI, TELECOM & Manage. SudParis, Evry

fYear

2009

fDate

19-24 April 2009

Firstpage

3237

Lastpage

3240

Abstract

This paper studies the asymptotic distribution of the eigenvectors estimated by some PCA algorithms based on the weighted subspace criterion. This enables us to analyse how the choice of the weighting matrix affects the algorithm´s performance, an issue previously overlooked.

Keywords

eigenvalues and eigenfunctions; matrix algebra; principal component analysis; PCA algorithm; asymptotic performance analysis; eigenvectors estimation; matrix; principal component analysis; weighted subspace criterion; Algorithm design and analysis; Approximation algorithms; Convergence; Covariance matrix; Eigenvalues and eigenfunctions; Performance analysis; Principal component analysis; Signal processing algorithms; Stochastic processes; Telecommunications; Principal component analysis; asymptotic performance analysis; weighted subspace criterion;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech and Signal Processing, 2009. ICASSP 2009. IEEE International Conference on

Conference_Location

Taipei

ISSN

1520-6149

Print_ISBN

978-1-4244-2353-8

Electronic_ISBN

1520-6149

Type

conf

DOI

10.1109/ICASSP.2009.4960314

Filename

4960314

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3524466