The Tucker decomposition is a higher-order analogue of the singular value decomposition
and is a popular method of performing analysis on multi-way data (tensors). Computing the …

HPC systems are increasingly used for data intensive computations which exhibit irregular
memory accesses, non-uniform work distributions, large memory footprints, and high …

Tensor decompositions are a powerful technique for enabling comprehensive and complete
analysis of real-world data. Data analysis through tensor decompositions involves intensive …

Presents a new algorithm for segmentation of noisy or textured images using the expectation-
maximization (EM) algorithm for estimating parameters of the probability mass function of the …

Tensor decomposition, the higher-order analogue to singular value decomposition, has
emerged as a useful tool for finding relationships in large, sparse, multidimensional data. As …

Tensor decompositions, which are factorizations of multi-dimensional arrays, are becoming
increasingly important in large-scale data analytics. A popular tensor decomposition …

In recent years, tensor-based machine learning methods, in which the Support Tensor
Machine (STM) is a typical technology, have gradually attracted the attention of researchers …

We propose a novel architecture to efficiently perform sparse tensor decomposition/
completion. As the generalization of vectors and matrices, tensors are widely used to …

Tensor factorization is a technique for analyzing data that features interactions of data along
three or more axes, or modes. Many fields such as retail, health analytics, and cybersecurity …

Evaluating Communication Costs for Distributed Sparse Tensor Factorization on Multi-GPU
Systems Page 1 Evaluating Communication Costs for Distributed Sparse Tensor Factorization …