A PLS-INTEGRATED LASSO METHOD WITH APPLICATION IN INDEX TRACKING

Shiqin Tang; Yining Dong; S. Joe Qin

doi:10.1109/ICASSP48485.2024.10448273

A PLS-INTEGRATED LASSO METHOD WITH APPLICATION IN INDEX TRACKING

Department of Data Science

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

Abstract

In traditional multivariate data analysis, dimension reduction and regression have been treated as distinct endeavors. Established techniques such as principal component regression (PCR) and partial least squares (PLS) regression traditionally compute latent components as intermediary steps-although with different underlying criteria-before proceeding with the regression analysis. In this paper, we introduce an innovative regression methodology named PLS-integrated Lasso (PLS-Lasso) that integrates the concept of dimension reduction directly into the regression process. We present two distinct formulations for PLS-Lasso, denoted as PLS-Lasso-v1 and PLS-Lasso-v2, along with clear and effective algorithms that ensure convergence to global optima. PLS-Lasso-v1 and PLS-Lasso-v2 are compared with Lasso on the task of financial index tracking and show promising results. © 2024 IEEE.

Original language	English
Title of host publication	2024 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings
Publisher	IEEE
Pages	6520-6524
ISBN (Electronic)	979-8-3503-4485-1
ISBN (Print)	979-8-3503-4486-8
DOIs	https://doi.org/10.1109/ICASSP48485.2024.10448273
Publication status	Published - 2024
Event	49th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2024) - COEX, Seoul, Korea, Republic of Duration: 14 Apr 2024 → 19 Apr 2024 https://2024.ieeeicassp.org/

Publication series

Name	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)	1520-6149
ISSN (Electronic)	2379-190X

Conference

Conference	49th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2024)
Place	Korea, Republic of
City	Seoul
Period	14/04/24 → 19/04/24
Internet address	https://2024.ieeeicassp.org/

Funding

This research work is supported by a Math and Application Project (2021YFA1003504) under the National Key R&D Program, a Collaborative Research Fund by RGC of Hong Kong (Project No. C1143-20G), a grant from the Natural Science Foundation of China (U20A20189), a grant from ITF - Guangdong-Hong Kong Technology Cooperation Funding Scheme (Project Ref. No. GHP/145/20), and an InnoHK initiative of The Government of the HKSAR for the Laboratory for AI-Powered Financial Technologies.

Research Keywords

Least absolute shrinkage and selection operator
Partial least squares regression
Statistical learning

RGC Funding Information

RGC-funded

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10.1109/ICASSP48485.2024.10448273

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Tang, S., Dong, Y., & Qin, S. J. (2024). A PLS-INTEGRATED LASSO METHOD WITH APPLICATION IN INDEX TRACKING. In 2024 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings (pp. 6520-6524). (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). IEEE. https://doi.org/10.1109/ICASSP48485.2024.10448273

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abstract = "In traditional multivariate data analysis, dimension reduction and regression have been treated as distinct endeavors. Established techniques such as principal component regression (PCR) and partial least squares (PLS) regression traditionally compute latent components as intermediary steps-although with different underlying criteria-before proceeding with the regression analysis. In this paper, we introduce an innovative regression methodology named PLS-integrated Lasso (PLS-Lasso) that integrates the concept of dimension reduction directly into the regression process. We present two distinct formulations for PLS-Lasso, denoted as PLS-Lasso-v1 and PLS-Lasso-v2, along with clear and effective algorithms that ensure convergence to global optima. PLS-Lasso-v1 and PLS-Lasso-v2 are compared with Lasso on the task of financial index tracking and show promising results. {\textcopyright} 2024 IEEE.",

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Tang, S , Dong, Y & Qin, SJ 2024, A PLS-INTEGRATED LASSO METHOD WITH APPLICATION IN INDEX TRACKING. in 2024 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, IEEE, pp. 6520-6524, 49th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2024), Seoul, Korea, Republic of, 14/04/24. https://doi.org/10.1109/ICASSP48485.2024.10448273

A PLS-INTEGRATED LASSO METHOD WITH APPLICATION IN INDEX TRACKING. / Tang, Shiqin ; Dong, Yining; Qin, S. Joe.
2024 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings. IEEE, 2024. p. 6520-6524 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

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AU - Tang, Shiqin

AU - Dong, Yining

AU - Qin, S. Joe

PY - 2024

Y1 - 2024

N2 - In traditional multivariate data analysis, dimension reduction and regression have been treated as distinct endeavors. Established techniques such as principal component regression (PCR) and partial least squares (PLS) regression traditionally compute latent components as intermediary steps-although with different underlying criteria-before proceeding with the regression analysis. In this paper, we introduce an innovative regression methodology named PLS-integrated Lasso (PLS-Lasso) that integrates the concept of dimension reduction directly into the regression process. We present two distinct formulations for PLS-Lasso, denoted as PLS-Lasso-v1 and PLS-Lasso-v2, along with clear and effective algorithms that ensure convergence to global optima. PLS-Lasso-v1 and PLS-Lasso-v2 are compared with Lasso on the task of financial index tracking and show promising results. © 2024 IEEE.

AB - In traditional multivariate data analysis, dimension reduction and regression have been treated as distinct endeavors. Established techniques such as principal component regression (PCR) and partial least squares (PLS) regression traditionally compute latent components as intermediary steps-although with different underlying criteria-before proceeding with the regression analysis. In this paper, we introduce an innovative regression methodology named PLS-integrated Lasso (PLS-Lasso) that integrates the concept of dimension reduction directly into the regression process. We present two distinct formulations for PLS-Lasso, denoted as PLS-Lasso-v1 and PLS-Lasso-v2, along with clear and effective algorithms that ensure convergence to global optima. PLS-Lasso-v1 and PLS-Lasso-v2 are compared with Lasso on the task of financial index tracking and show promising results. © 2024 IEEE.

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Y2 - 14 April 2024 through 19 April 2024

ER -

A PLS-INTEGRATED LASSO METHOD WITH APPLICATION IN INDEX TRACKING

Abstract

Publication series

Conference

Funding

Research Keywords

RGC Funding Information

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ITF: Aerospace System Prognostics and Health Management Model and Telemetry Task Optimization

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A PLS-INTEGRATED LASSO METHOD WITH APPLICATION IN INDEX TRACKING

Abstract

Publication series

Conference

Funding

Research Keywords

RGC Funding Information

Access Output

Other Access Options

Permanent Link

Other Files and Links

Fingerprint

Projects

ITF: Aerospace System Prognostics and Health Management Model and Telemetry Task Optimization

Cite this