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 ...

Osteoinductive low-dose 3D porous calcium phosphate graphene oxide–integrated matrices enhance osteogenesis and mechanical properties ...

The paper compares of the multidimensional matrix algebra and the tensor algebra. It is shown that tensor algebra operations are realized in the multidimensional matrix algebra more efficiently.

GrAPL 2021 Keynote 1: Sparse Adjacency Matrices at the Core of Graph Databases: GraphBLAS the Engine Behind RedisGraph Property Graph Database ...

This paper introduces matrix product state (MPS) decomposition as a new and systematic method to compress multidimensional data represented by higher order tensors. It solves two major bottlenecks in ...

In the eight-point linear algorithm for determining 3D motion/structure from two perspective views using point correspondences, the E matrix plays a central role. The E matrix is defined as a ...

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 ...