Decentralized Rank-Adaptive Matrix Factorization-Part I: Algorithm Development

Factorizing a low-rank matrix into two matrix factors with low dimensions from its noisy observations is a classical but challenging problem arising from real-world applications. This paper develops decentralized matrix factorization algorithms, i.e., factorizing a matrix whose columns are stored di...

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Vydané v:	IEEE transactions on signal processing Ročník 73; s. 4124 - 4140
Hlavní autori:	Jiao, Yuchen, Gu, Yuantao, Chang, Tsung-Hui, Luo, Zhi-Quan
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Accuracy Adaptive algorithms Algorithms Convergence decentralized Factorization Image reconstruction Linear programming Low rank Matrix decomposition matrix factorization Noise Principal component analysis rank adaptive Regularization Reviews Signal processing algorithms Sparse matrices
ISSN:	1053-587X, 1941-0476
On-line prístup:	Získať plný text
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