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- W4298103372 abstract "Because of the attractiveness of the canonical polyadic (CP) tensor decomposition in various applications, several algorithms have been designed to compute it, but efficient ones are still lacking. Iterative deflation algorithms based on successive rank-1 approximations can be used to perform this task, since the latter are rather easy to compute. We first present an algebraic rank-1 approximation method that performs better than the standard higher-order singular value decomposition (HOSVD) for three-way tensors. Second, we propose a new iterative rank-1 approximation algorithm that improves any other rank-1 approximation method. Third, we describe a probabilistic framework allowing to study the convergence of deflation CP decomposition (DCPD) algorithms based on successive rank-1 approximations. A set of computer experiments then validates theoretical results and demonstrates the efficiency of DCPD algorithms compared to other ones." @default.
- W4298103372 created "2022-10-01" @default.
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- W4298103372 date "2015-08-21" @default.
- W4298103372 modified "2023-09-26" @default.
- W4298103372 title "Rank-1 Tensor Approximation Methods and Application to Deflation" @default.
- W4298103372 doi "https://doi.org/10.48550/arxiv.1508.05273" @default.
- W4298103372 hasPublicationYear "2015" @default.
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