Nonnegative Tensor Train Factorization with DMRG Technique
- Authors: Shcherbakova E.M.1,2
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Affiliations:
- Lomonosov Moscow State University
- Marchuk Institute of Numerical Mathematics of Russian Academy of Sciences
- Issue: Vol 40, No 11 (2019)
- Pages: 1863-1872
- Section: Article
- URL: https://journals.rcsi.science/1995-0802/article/view/206095
- DOI: https://doi.org/10.1134/S1995080219110283
- ID: 206095
Cite item
Abstract
Tensor train is one of the modern decompositions used as low-rank tensor approximations of multidimensional arrays. If the original data is nonnegative we sometimes want the approximant to keep this property. In this work new methods for nonnegative tensor train factorization are proposed. Low-rank approximation approach helps to speed up the computations whereas DMRG technique allows to adapt nonnegative TT ranks for better accuracy. The performance analysis of the proposed algorithms as well as comparison with other nonnegative TT factorization method are presented.
Keywords
About the authors
E. M. Shcherbakova
Lomonosov Moscow State University; Marchuk Institute of Numerical Mathematics of Russian Academy of Sciences
Author for correspondence.
Email: lena19592@mail.ru
Russian Federation, Moscow, 119991; Moscow, 119333
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