The canonical polyadic decomposition (CPD) of high-order tensors, also known as Candecomp/Parafac, is very useful for representing and analyzing multidimensional data. This paper considers a CPD model ...

Low-rank tensor completion has attracted much interest in many applications such as image processing, data mining and machine learning. A widely used method is to minimize the sum of nuclear norms of ...

We investigate a novel approach to approximate tensor-network contraction via the exact, matrix-free decomposition of full tensor-networks. We study this method as a means to eliminate the propagat ...

During the past 20 years, low-rank tensor and matrix decomposition models (LRDMs) have become indispensable tools for signal processing, machine learning, and data science. LRDMs represent ...

Clean adjacency matrices are then used to perform orthogonal symmetric non-negative matrix factorization, extracting latent representations of the multi-layer networks. Moreover, the nuclear norm is ...

We investigate the efficient combination of the canonical polyadic decomposition (CPD) and tensor hyper-contraction (THC) approaches. We first present a novel low-cost CPD solver that leverages a ...

Coupled matrix-tensor factorizations have proved to be a powerful tool for data fusion problems in a variety of applications. Uniqueness conditions for such coupled decompositions have only recently ...

These concepts afford a number of theoretical results that clarify the connections between symmetric and antisymmetric components in tensor fields. In addition, these manifolds naturally lead to ...