Аннотация:Abstract—Tensor train model is a low-rank approximation for multidimensional data. In this article we demonstrate how it can be succesfully used for fast computation of nonnegative tensor train,nonnegative canonical and nonnegative Tucker factorizations. The proposed approaches can beincorporated in wide range of methods to solve big data problems.DOI: 10.1134/S1995080222070228Keywords and phrases: nonnegative tensor factorization, tensor train, Tucker decomposition,canonical decomposition.