Tensor analysis has emerged as a fundamental mathematical framework in elucidating complex interactions within quantum physics and elasticity. In quantum physics, tensors facilitate the description of ...

Abstract: Vector data are normally used for probabilistic graphical models with Bayesian inference. However, tensor data, i.e., multidimensional arrays, are actually natural representations of a large ...

This repository demonstrates how to apply Canonical Polyadic (CP) decomposition on simulated EEG data using the tensorly library in Python.

Classical regression methods treat covariates as a vector and estimate a corresponding vector of regression coefficients. Modern applications in medical imaging generate covariates of more complex ...

1 Department of Computer Science, Nagoya Institute of Technology, Aichi, Japan 2 RIKEN Center for Advanced Intelligence Project, Tokyo, Japan In recent years, a learning method for classifiers using ...

Abstract: This paper addresses the tensor robust principal component analysis (RPCA) by employing linear slim transforms along the mode-3 of the tensor. Previous works have empirically shown the ...

Sparse tensor operations are increasingly important in diverse applications such as social networks, deep learning, diagnosis, crime, and review analysis. However, a major obstacle in sparse tensor ...