Tensor neural network models for tensor singular value decompositions

Autor:	Yimin Wei, Xuezhong Wang, Maolin Che
Rok vydání:	2020
Předmět:	Left and right 021103 operations research Control and Optimization Artificial neural network Applied Mathematics 0211 other engineering and technologies 010103 numerical & computational mathematics 02 engineering and technology 01 natural sciences Algebra Computational Mathematics Singular value Convergence (routing) Tensor 0101 mathematics Representation (mathematics) Subspace topology Mathematics
Zdroj:	Computational Optimization and Applications. 75:753-777
ISSN:	1573-2894 0926-6003
DOI:	10.1007/s10589-020-00167-1
Popis:	Tensor decompositions have become increasingly prevalent in recent years. Traditionally, tensors are represented or decomposed as a sum of rank-one outer products using either the CANDECOMP/PARAFAC, the Tucker model, or some variations thereof. The motivation of these decompositions is to find an approximate representation for a given tensor. The main propose of this paper is to develop two neural network models for finding an approximation based on t-product for a given third-order tensor. Theoretical analysis shows that each of the neural network models ensures the convergence performance. The computer simulation results further substantiate that the models can find effectively the left and right singular tensor subspace.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::c9044d129118d11cc5b935ff66250748 https://doi.org/10.1007/s10589-020-00167-1 Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
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