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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Bibliographic Details

Published in:	Computational optimization and applications Vol. 91; no. 1; pp. 201 - 233
Main Authors:	Liu, Zehui, Wang, Qingsong, Cui, Chunfeng, Xia, Yong
Format:	Journal Article
Language:	English
Published:	New York Springer Nature B.V 01.05.2025
Subjects:	Algorithms Binary data Convergence Decomposition Euclidean geometry Lipschitz condition Tensors
ISSN:	0926-6003, 1573-2894
Online Access:	Get full text
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