Randomized algorithms for the low multilinear rank approximations of tensors
نویسندگان
چکیده
In this paper, we focus on developing randomized algorithms for the computation of low multilinear rank approximations tensors based random projection and singular value decomposition. Following theory values sub-Gaussian matrices, make a probabilistic analysis error bounds algorithm. We demonstrate effectiveness proposed via several numerical examples.
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ژورنال
عنوان ژورنال: Journal of Computational and Applied Mathematics
سال: 2021
ISSN: ['0377-0427', '1879-1778', '0771-050X']
DOI: https://doi.org/10.1016/j.cam.2020.113380