Nonnegative Tensor Train Factorization with DMRG Technique
- Авторлар: Shcherbakova E.1,2
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Мекемелер:
- Lomonosov Moscow State University
- Marchuk Institute of Numerical Mathematics of Russian Academy of Sciences
- Шығарылым: Том 40, № 11 (2019)
- Беттер: 1863-1872
- Бөлім: Article
- URL: https://journals.rcsi.science/1995-0802/article/view/206095
- DOI: https://doi.org/10.1134/S1995080219110283
- ID: 206095
Дәйексөз келтіру
Аннотация
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.
Негізгі сөздер
Авторлар туралы
E. Shcherbakova
Lomonosov Moscow State University; Marchuk Institute of Numerical Mathematics of Russian Academy of Sciences
Хат алмасуға жауапты Автор.
Email: lena19592@mail.ru
Ресей, Moscow, 119991; Moscow, 119333