Fast principal component analysis and data whitening algorithms

Messaoud Thameri; Abla Kammoun; Karim Abed-Meraim; Adel Belouchrani

doi:10.1109/WOSSPA.2011.5931434

Fast principal component analysis and data whitening algorithms

Messaoud Thameri^*, Abla Kammoun, Karim Abed-Meraim, Adel Belouchrani

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

9 Scopus citations

Abstract

In this paper, we propose an adaptive implementation of a fast-convergent algorithm for principal component extraction. Our approach consists of first estimating a basis of the principal subspace through the use of OPAST algorithm. The obtained basis is then fed to a second process where at each iteration one or several Givens transformations are applied to estimate the principal components. Later on, the proposed PCA algorithm is used to derive a fast data whitening solution that overcomes the existing ones of similar complexity order. Simulation results support the high performance of our algorithms in terms of accuracy and speed of convergence.

Original language	English (US)
Title of host publication	7th International Workshop on Systems, Signal Processing and their Applications, WoSSPA 2011
Pages	139-142
Number of pages	4
DOIs	https://doi.org/10.1109/WOSSPA.2011.5931434
State	Published - 2011
Externally published	Yes
Event	7th International Workshop on Systems, Signal Processing and their Applications, WoSSPA 2011 - Tipaza, Algeria Duration: May 9 2011 → May 11 2011

Other

Other	7th International Workshop on Systems, Signal Processing and their Applications, WoSSPA 2011
Country/Territory	Algeria
City	Tipaza
Period	05/9/11 → 05/11/11

Keywords

Adaptive algorithms
Data whitening
Givens rotation
Principal component analysis
Principal Subspace tracking

ASJC Scopus subject areas

Computer Networks and Communications
Signal Processing

Access to Document

10.1109/WOSSPA.2011.5931434

Cite this

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title = "Fast principal component analysis and data whitening algorithms",

abstract = "In this paper, we propose an adaptive implementation of a fast-convergent algorithm for principal component extraction. Our approach consists of first estimating a basis of the principal subspace through the use of OPAST algorithm. The obtained basis is then fed to a second process where at each iteration one or several Givens transformations are applied to estimate the principal components. Later on, the proposed PCA algorithm is used to derive a fast data whitening solution that overcomes the existing ones of similar complexity order. Simulation results support the high performance of our algorithms in terms of accuracy and speed of convergence.",

keywords = "Adaptive algorithms, Data whitening, Givens rotation, Principal component analysis, Principal Subspace tracking",

author = "Messaoud Thameri and Abla Kammoun and Karim Abed-Meraim and Adel Belouchrani",

year = "2011",

doi = "10.1109/WOSSPA.2011.5931434",

language = "English (US)",

isbn = "9781457706905",

pages = "139--142",

booktitle = "7th International Workshop on Systems, Signal Processing and their Applications, WoSSPA 2011",

note = "7th International Workshop on Systems, Signal Processing and their Applications, WoSSPA 2011 ; Conference date: 09-05-2011 Through 11-05-2011",

}

Thameri, M, Kammoun, A, Abed-Meraim, K & Belouchrani, A 2011, Fast principal component analysis and data whitening algorithms. in 7th International Workshop on Systems, Signal Processing and their Applications, WoSSPA 2011., 5931434, pp. 139-142, 7th International Workshop on Systems, Signal Processing and their Applications, WoSSPA 2011, Tipaza, Algeria, 05/9/11. https://doi.org/10.1109/WOSSPA.2011.5931434

TY - GEN

T1 - Fast principal component analysis and data whitening algorithms

AU - Thameri, Messaoud

AU - Kammoun, Abla

AU - Abed-Meraim, Karim

AU - Belouchrani, Adel

PY - 2011

Y1 - 2011

N2 - In this paper, we propose an adaptive implementation of a fast-convergent algorithm for principal component extraction. Our approach consists of first estimating a basis of the principal subspace through the use of OPAST algorithm. The obtained basis is then fed to a second process where at each iteration one or several Givens transformations are applied to estimate the principal components. Later on, the proposed PCA algorithm is used to derive a fast data whitening solution that overcomes the existing ones of similar complexity order. Simulation results support the high performance of our algorithms in terms of accuracy and speed of convergence.

AB - In this paper, we propose an adaptive implementation of a fast-convergent algorithm for principal component extraction. Our approach consists of first estimating a basis of the principal subspace through the use of OPAST algorithm. The obtained basis is then fed to a second process where at each iteration one or several Givens transformations are applied to estimate the principal components. Later on, the proposed PCA algorithm is used to derive a fast data whitening solution that overcomes the existing ones of similar complexity order. Simulation results support the high performance of our algorithms in terms of accuracy and speed of convergence.

KW - Adaptive algorithms

KW - Data whitening

KW - Givens rotation

KW - Principal component analysis

KW - Principal Subspace tracking

UR - http://www.scopus.com/inward/record.url?scp=79960927054&partnerID=8YFLogxK

U2 - 10.1109/WOSSPA.2011.5931434

DO - 10.1109/WOSSPA.2011.5931434

M3 - Conference contribution

AN - SCOPUS:79960927054

SN - 9781457706905

SP - 139

EP - 142

BT - 7th International Workshop on Systems, Signal Processing and their Applications, WoSSPA 2011

T2 - 7th International Workshop on Systems, Signal Processing and their Applications, WoSSPA 2011

Y2 - 9 May 2011 through 11 May 2011

ER -

Fast principal component analysis and data whitening algorithms

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