Inertial accelerated stochastic mirror descent for large-scale generalized tensor CP decomposition

The majority of classic tensor CP decomposition models are designed for squared loss, utilizing Euclidean distance as a local proximal term. However, the Euclidean distance is unsuitable for the generalized loss function applicable to diverse types of real-world data, such as integer and binary data...

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Vydané v:	Computational optimization and applications Ročník 91; číslo 1; s. 201 - 233
Hlavní autori:	Liu, Zehui, Wang, Qingsong, Cui, Chunfeng, Xia, Yong
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer Nature B.V 01.05.2025
Predmet:	Algorithms Binary data Convergence Decomposition Euclidean geometry Lipschitz condition Tensors
ISSN:	0926-6003, 1573-2894
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