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International Journal of Machine Learning and Cybernetics
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06-08-2024 | Original Article

A generalized tri-factorization method for accurate matrix completion

Authors: Qing Liu, Hao Wu, Yu Zong, Zheng-Yu Liu

Published in: International Journal of Machine Learning and Cybernetics | Issue 12/2024

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Abstract

The article introduces a novel generalized tri-factorization method (GTF) for accurate matrix completion, addressing the limitations of existing methods such as the Fast Tri-Factorization (FTF) method. GTF employs a generalized norm optimization model and QR decomposition, which significantly enhances both the speed and accuracy of matrix recovery. The method is designed to handle complex matrices and structured missing blocks, making it suitable for various applications including image processing, data mining, and recommendation systems. The article provides a detailed explanation of the GTF method, its optimization process, and experimental results demonstrating its superior performance compared to other state-of-the-art methods. The experimental results show that GTF outperforms FTF, Schatten Capped P Norm minimization, and other nuclear norm minimization-based methods in terms of both convergence accuracy and computational efficiency. This makes GTF a promising solution for high-precision matrix completion tasks.
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Metadata
Title
A generalized tri-factorization method for accurate matrix completion
Authors
Qing Liu
Hao Wu
Yu Zong
Zheng-Yu Liu
Publication date
06-08-2024
Publisher
Springer Berlin Heidelberg
Published in
International Journal of Machine Learning and Cybernetics / Issue 12/2024
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-024-02289-y

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