Automatic model-order selection for PCA

Michel Sarkis; Zaher Dawy; Florian Obermeier; Klaus Diepold

doi:10.1109/ICIP.2006.312628

Automatic model-order selection for PCA

Michel Sarkis
, Zaher Dawy
, Florian Obermeier
, Klaus Diepold

Chair of Data Processing

Technical University of Munich
American University of Beirut

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

2 Scopus citations

Abstract

Determining the model-order of a given data set is an important task in signal analysis. Principal Component Analysis (PCA) can be used for this purpose if there is a criterion upon which the correct order can be chosen. In this work, we propose a new and simple technique to determine automatically the rank of a PCA model. Tested with simulated data, the algorithm is able to determine the correct model order efficiently. Applied to video sequences, this method is able to estimate the necessary subspaces that capture the motion and illuminance changes within the different frames. This helps in reducing the storage need/requirements of video sequences and improves the efficiency of context based search and retrieval techniques.

Original language	English
Title of host publication	2006 IEEE International Conference on Image Processing, ICIP 2006 - Proceedings
Pages	933-936
Number of pages	4
DOIs	https://doi.org/10.1109/ICIP.2006.312628
State	Published - 2006
Event	2006 IEEE International Conference on Image Processing, ICIP 2006 - Atlanta, GA, United States Duration: 8 Oct 2006 → 11 Oct 2006

Publication series

Name	Proceedings - International Conference on Image Processing, ICIP
ISSN (Print)	1522-4880

Conference

Conference	2006 IEEE International Conference on Image Processing, ICIP 2006
Country/Territory	United States
City	Atlanta, GA
Period	8/10/06 → 11/10/06

Keywords

Data compression
Image coding
Information retrieval
Video signal processing

Access to Document

10.1109/ICIP.2006.312628

Cite this

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title = "Automatic model-order selection for PCA",

abstract = "Determining the model-order of a given data set is an important task in signal analysis. Principal Component Analysis (PCA) can be used for this purpose if there is a criterion upon which the correct order can be chosen. In this work, we propose a new and simple technique to determine automatically the rank of a PCA model. Tested with simulated data, the algorithm is able to determine the correct model order efficiently. Applied to video sequences, this method is able to estimate the necessary subspaces that capture the motion and illuminance changes within the different frames. This helps in reducing the storage need/requirements of video sequences and improves the efficiency of context based search and retrieval techniques.",

keywords = "Data compression, Image coding, Information retrieval, Video signal processing",

author = "Michel Sarkis and Zaher Dawy and Florian Obermeier and Klaus Diepold",

year = "2006",

doi = "10.1109/ICIP.2006.312628",

language = "English",

isbn = "1424404819",

series = "Proceedings - International Conference on Image Processing, ICIP",

pages = "933--936",

booktitle = "2006 IEEE International Conference on Image Processing, ICIP 2006 - Proceedings",

note = "2006 IEEE International Conference on Image Processing, ICIP 2006 ; Conference date: 08-10-2006 Through 11-10-2006",

}

Sarkis, M, Dawy, Z, Obermeier, F & Diepold, K 2006, Automatic model-order selection for PCA. in 2006 IEEE International Conference on Image Processing, ICIP 2006 - Proceedings., 4106684, Proceedings - International Conference on Image Processing, ICIP, pp. 933-936, 2006 IEEE International Conference on Image Processing, ICIP 2006, Atlanta, GA, United States, 8/10/06. https://doi.org/10.1109/ICIP.2006.312628

Automatic model-order selection for PCA. / Sarkis, Michel; Dawy, Zaher; Obermeier, Florian et al.
2006 IEEE International Conference on Image Processing, ICIP 2006 - Proceedings. 2006. p. 933-936 4106684 (Proceedings - International Conference on Image Processing, ICIP).

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

TY - GEN

T1 - Automatic model-order selection for PCA

AU - Sarkis, Michel

AU - Dawy, Zaher

AU - Obermeier, Florian

AU - Diepold, Klaus

PY - 2006

Y1 - 2006

N2 - Determining the model-order of a given data set is an important task in signal analysis. Principal Component Analysis (PCA) can be used for this purpose if there is a criterion upon which the correct order can be chosen. In this work, we propose a new and simple technique to determine automatically the rank of a PCA model. Tested with simulated data, the algorithm is able to determine the correct model order efficiently. Applied to video sequences, this method is able to estimate the necessary subspaces that capture the motion and illuminance changes within the different frames. This helps in reducing the storage need/requirements of video sequences and improves the efficiency of context based search and retrieval techniques.

AB - Determining the model-order of a given data set is an important task in signal analysis. Principal Component Analysis (PCA) can be used for this purpose if there is a criterion upon which the correct order can be chosen. In this work, we propose a new and simple technique to determine automatically the rank of a PCA model. Tested with simulated data, the algorithm is able to determine the correct model order efficiently. Applied to video sequences, this method is able to estimate the necessary subspaces that capture the motion and illuminance changes within the different frames. This helps in reducing the storage need/requirements of video sequences and improves the efficiency of context based search and retrieval techniques.

KW - Data compression

KW - Image coding

KW - Information retrieval

KW - Video signal processing

UR - https://www.scopus.com/pages/publications/78649860751

U2 - 10.1109/ICIP.2006.312628

DO - 10.1109/ICIP.2006.312628

M3 - Conference contribution

AN - SCOPUS:78649860751

SN - 1424404819

SN - 9781424404810

T3 - Proceedings - International Conference on Image Processing, ICIP

SP - 933

EP - 936

BT - 2006 IEEE International Conference on Image Processing, ICIP 2006 - Proceedings

T2 - 2006 IEEE International Conference on Image Processing, ICIP 2006

Y2 - 8 October 2006 through 11 October 2006

ER -

Automatic model-order selection for PCA

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Keywords

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