The emergence of big data and the multidimensional nature of wireless communication
signals present significant opportunities for exploiting the versatility of tensor …

Independent component analysis (ICA) is a data analysis tool that can be seen as a
refinement of principal component analysis or factor analysis. ICA recovers the structures in …

Minimizing the discrepancy of feature distributions between different domains is one of the
most promising directions in unsupervised domain adaptation. From the perspective of …

This work considers a computationally and statistically efficient parameter estimation method
for a wide class of latent variable models—including Gaussian mixture models, hidden …

Edited by the people who were forerunners in creating the field, together with contributions
from 34 leading international experts, this handbook provides the definitive reference on …

This survey provides an overview of higher-order tensor decompositions, their applications,
and available software. A tensor is a multidimensional or-way array. Decompositions of …

Many real-world problems involve the optimization of multiple, possibly conflicting
objectives. Multi-objective reinforcement learning (MORL) is a generalization of standard …

We develop efficient algorithms for estimating low-degree moments of unknown distributions
in the presence of adversarial outliers and design a new family of convex relaxations for k …

With the increasing availability of various sensor technologies, we now have access to large
amounts of multiblock (also called multiset, multirelational, or multiview) data that need to be …

Higher order tensor inversion is possible for even order. This is due to the fact that a tensor
group endowed with the contracted product is isomorphic to the general linear group of …