Robust Matrix Completion via Novel M-Estimator Functions

Zhi-Yong Wang; Hing Cheung So

doi:10.1109/ICECET61485.2024.10698047

Robust Matrix Completion via Novel M-Estimator Functions

^*Corresponding author for this work

Department of Electrical Engineering

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

1 Citation (Scopus)

Abstract

As most existing M-estimators down-weigh clean observations which are not corrupted by outliers when resisting gross errors, we put forward a framework to produce numerous new robust loss functions, which only penalize outlier-contaminated entries, via combining the quadratic functions and the commonly-used M-estimators. We then apply the Welsch, Cauchy and ℓ_p-norm functions to the devised framework and propose some novel M-estimators. Besides, based on the developed robust loss functions, efficient robust matrix completion algorithms with convergence guarantees are exploited. Experimental results verify the effectiveness of the proposed approaches over the competitors. Matlab codes are available at https://github.com/bestzywang. © 2024 IEEE.

Original language	English
Title of host publication	2024 International Conference on Electrical, Computer and Energy Technologies (ICECET)
Publisher	IEEE
Number of pages	5
ISBN (Electronic)	9798350395914
ISBN (Print)	979-8-3503-9592-1
DOIs	https://doi.org/10.1109/ICECET61485.2024.10698047
Publication status	Published - 2024
Event	4th IEEE International Conference on Electrical, Computer, and Energy Technologies (ICECET 2024) - Sydney, Australia Duration: 25 Jul 2024 → 27 Jul 2024 https://www.icecet.com/2024/

Publication series

Name	International Conference on Electrical, Computer, and Energy Technologies, ICECET

Conference

Conference	4th IEEE International Conference on Electrical, Computer, and Energy Technologies (ICECET 2024)
Country/Territory	Australia
City	Sydney
Period	25/07/24 → 27/07/24
Internet address	https://www.icecet.com/2024/

Funding

This work was supported in part by the Research Grants Council of the Hong Kong Special Administrative Region, China [Project No. CityU 11207922], and in part by the Research Grants of Shenzhen Research Institute, City University of Hong Kong, Shenzhen, China [Project No. R-IND25501].

Research Keywords

implicit regularizer
low-rank
matrix completion
Matrix factorization
outlier-robustness

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10.1109/ICECET61485.2024.10698047

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title = "Robust Matrix Completion via Novel M-Estimator Functions",

abstract = "As most existing M-estimators down-weigh clean observations which are not corrupted by outliers when resisting gross errors, we put forward a framework to produce numerous new robust loss functions, which only penalize outlier-contaminated entries, via combining the quadratic functions and the commonly-used M-estimators. We then apply the Welsch, Cauchy and ℓp-norm functions to the devised framework and propose some novel M-estimators. Besides, based on the developed robust loss functions, efficient robust matrix completion algorithms with convergence guarantees are exploited. Experimental results verify the effectiveness of the proposed approaches over the competitors. Matlab codes are available at https://github.com/bestzywang. {\textcopyright} 2024 IEEE.",

keywords = "implicit regularizer, low-rank, matrix completion, Matrix factorization, outlier-robustness",

author = "Zhi-Yong Wang and So, {Hing Cheung}",

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booktitle = "2024 International Conference on Electrical, Computer and Energy Technologies (ICECET)",

address = "United States",

note = "4th IEEE International Conference on Electrical, Computer, and Energy Technologies (ICECET 2024) ; Conference date: 25-07-2024 Through 27-07-2024",

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Wang, Z-Y & So, HC 2024, Robust Matrix Completion via Novel M-Estimator Functions. in 2024 International Conference on Electrical, Computer and Energy Technologies (ICECET). International Conference on Electrical, Computer, and Energy Technologies, ICECET, IEEE, 4th IEEE International Conference on Electrical, Computer, and Energy Technologies (ICECET 2024), Sydney, Australia, 25/07/24. https://doi.org/10.1109/ICECET61485.2024.10698047

Robust Matrix Completion via Novel M-Estimator Functions. / Wang, Zhi-Yong ; So, Hing Cheung.
2024 International Conference on Electrical, Computer and Energy Technologies (ICECET). IEEE, 2024. (International Conference on Electrical, Computer, and Energy Technologies, ICECET).

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

TY - GEN

T1 - Robust Matrix Completion via Novel M-Estimator Functions

AU - Wang, Zhi-Yong

AU - So, Hing Cheung

PY - 2024

Y1 - 2024

N2 - As most existing M-estimators down-weigh clean observations which are not corrupted by outliers when resisting gross errors, we put forward a framework to produce numerous new robust loss functions, which only penalize outlier-contaminated entries, via combining the quadratic functions and the commonly-used M-estimators. We then apply the Welsch, Cauchy and ℓp-norm functions to the devised framework and propose some novel M-estimators. Besides, based on the developed robust loss functions, efficient robust matrix completion algorithms with convergence guarantees are exploited. Experimental results verify the effectiveness of the proposed approaches over the competitors. Matlab codes are available at https://github.com/bestzywang. © 2024 IEEE.

AB - As most existing M-estimators down-weigh clean observations which are not corrupted by outliers when resisting gross errors, we put forward a framework to produce numerous new robust loss functions, which only penalize outlier-contaminated entries, via combining the quadratic functions and the commonly-used M-estimators. We then apply the Welsch, Cauchy and ℓp-norm functions to the devised framework and propose some novel M-estimators. Besides, based on the developed robust loss functions, efficient robust matrix completion algorithms with convergence guarantees are exploited. Experimental results verify the effectiveness of the proposed approaches over the competitors. Matlab codes are available at https://github.com/bestzywang. © 2024 IEEE.

KW - implicit regularizer

KW - low-rank

KW - matrix completion

KW - Matrix factorization

KW - outlier-robustness

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DO - 10.1109/ICECET61485.2024.10698047

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 979-8-3503-9592-1

T3 - International Conference on Electrical, Computer, and Energy Technologies, ICECET

BT - 2024 International Conference on Electrical, Computer and Energy Technologies (ICECET)

PB - IEEE

T2 - 4th IEEE International Conference on Electrical, Computer, and Energy Technologies (ICECET 2024)

Y2 - 25 July 2024 through 27 July 2024

ER -

Robust Matrix Completion via Novel M-Estimator Functions

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