Critical Vector Learning to Construct Sparse Kernel Modeling with PRESS Statistic

Junbin Gao; Lei Zhang; D Shi

doi:10.1109/ICMLC.2004.1378591

Critical Vector Learning to Construct Sparse Kernel Modeling with PRESS Statistic

Junbin Gao, Lei Zhang, D Shi

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

1 Citation (Scopus)

Abstract

A novel critical vector (CV) regression algorithm is proposed in the paper based on our previous work and PRESS statistics. The proposed regularized CV algorithm finds critical vectors in a successive greedy process in which, compared to the classical OLS algorithm, the orthogonalization has been removed from the algorithm. The performance of the proposed algorithm is comparable to the OLS algorithm while it saves a lot of time complexities in implementing orthogonalization needed in the OLS algorithm.

Original language	English
Title of host publication	Proceedings of 2004 International Conference On Machine Learning and Cybernetics
Editors	Yeung, D S
Place of Publication	Los Alamitos, United States of America
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	3223-3228
Volume	Volume 5, 26-29 Aug
ISBN (Print)	0780384032
DOIs	https://doi.org/10.1109/ICMLC.2004.1378591
Publication status	Published - 2004
Event	ICMLC 2004: 2004 International Conference on Machine Learning and Cybernetics - Shanghai, China Duration: 26 Aug 2004 → 29 Aug 2004

Conference

Conference	ICMLC 2004: 2004 International Conference on Machine Learning and Cybernetics
City	Shanghai, China
Period	26/08/04 → 29/08/04

Keywords

Simulation and Modelling

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10.1109/ICMLC.2004.1378591

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Gao, J., Zhang, L., & Shi, D. (2004). Critical Vector Learning to Construct Sparse Kernel Modeling with PRESS Statistic. In Yeung, & D. S (Eds.), Proceedings of 2004 International Conference On Machine Learning and Cybernetics (Vol. Volume 5, 26-29 Aug, pp. 3223-3228). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ICMLC.2004.1378591

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title = "Critical Vector Learning to Construct Sparse Kernel Modeling with PRESS Statistic",

abstract = "A novel critical vector (CV) regression algorithm is proposed in the paper based on our previous work and PRESS statistics. The proposed regularized CV algorithm finds critical vectors in a successive greedy process in which, compared to the classical OLS algorithm, the orthogonalization has been removed from the algorithm. The performance of the proposed algorithm is comparable to the OLS algorithm while it saves a lot of time complexities in implementing orthogonalization needed in the OLS algorithm.",

keywords = "Simulation and Modelling",

author = "Junbin Gao and Lei Zhang and D Shi",

year = "2004",

doi = "10.1109/ICMLC.2004.1378591",

language = "English",

isbn = "0780384032",

volume = "Volume 5, 26-29 Aug",

pages = "3223--3228",

editor = "Yeung and D S",

booktitle = "Proceedings of 2004 International Conference On Machine Learning and Cybernetics",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

note = "ICMLC 2004: 2004 International Conference on Machine Learning and Cybernetics ; Conference date: 26-08-2004 Through 29-08-2004",

}

Gao, J, Zhang, L & Shi, D 2004, Critical Vector Learning to Construct Sparse Kernel Modeling with PRESS Statistic. in Yeung & D S (eds), Proceedings of 2004 International Conference On Machine Learning and Cybernetics. vol. Volume 5, 26-29 Aug, Institute of Electrical and Electronics Engineers (IEEE), Los Alamitos, United States of America, pp. 3223-3228, ICMLC 2004: 2004 International Conference on Machine Learning and Cybernetics, Shanghai, China, 26/08/04. https://doi.org/10.1109/ICMLC.2004.1378591

Critical Vector Learning to Construct Sparse Kernel Modeling with PRESS Statistic. / Gao, Junbin; Zhang, Lei; Shi, D.
Proceedings of 2004 International Conference On Machine Learning and Cybernetics. ed. / Yeung; D S. Vol. Volume 5, 26-29 Aug Los Alamitos, United States of America: Institute of Electrical and Electronics Engineers (IEEE), 2004. p. 3223-3228.

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

TY - GEN

T1 - Critical Vector Learning to Construct Sparse Kernel Modeling with PRESS Statistic

AU - Gao, Junbin

AU - Zhang, Lei

AU - Shi, D

PY - 2004

Y1 - 2004

N2 - A novel critical vector (CV) regression algorithm is proposed in the paper based on our previous work and PRESS statistics. The proposed regularized CV algorithm finds critical vectors in a successive greedy process in which, compared to the classical OLS algorithm, the orthogonalization has been removed from the algorithm. The performance of the proposed algorithm is comparable to the OLS algorithm while it saves a lot of time complexities in implementing orthogonalization needed in the OLS algorithm.

AB - A novel critical vector (CV) regression algorithm is proposed in the paper based on our previous work and PRESS statistics. The proposed regularized CV algorithm finds critical vectors in a successive greedy process in which, compared to the classical OLS algorithm, the orthogonalization has been removed from the algorithm. The performance of the proposed algorithm is comparable to the OLS algorithm while it saves a lot of time complexities in implementing orthogonalization needed in the OLS algorithm.

KW - Simulation and Modelling

UR - http://www.icmlc.com/icmlc_welcome.htm

UR - https://www.scopus.com/pages/publications/6344280394

U2 - 10.1109/ICMLC.2004.1378591

DO - 10.1109/ICMLC.2004.1378591

M3 - Conference contribution

SN - 0780384032

VL - Volume 5, 26-29 Aug

SP - 3223

EP - 3228

BT - Proceedings of 2004 International Conference On Machine Learning and Cybernetics

A2 - Yeung, null

A2 - S, D

PB - Institute of Electrical and Electronics Engineers (IEEE)

CY - Los Alamitos, United States of America

T2 - ICMLC 2004: 2004 International Conference on Machine Learning and Cybernetics

Y2 - 26 August 2004 through 29 August 2004

ER -

Gao J, Zhang L, Shi D. Critical Vector Learning to Construct Sparse Kernel Modeling with PRESS Statistic. In Yeung, S D, editors, Proceedings of 2004 International Conference On Machine Learning and Cybernetics. Vol. Volume 5, 26-29 Aug. Los Alamitos, United States of America: Institute of Electrical and Electronics Engineers (IEEE). 2004. p. 3223-3228 doi: 10.1109/ICMLC.2004.1378591

Critical Vector Learning to Construct Sparse Kernel Modeling with PRESS Statistic

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