Lower Bound on Tensor Rank
Abstract
Tensors and tensor decompositions are natural tools to analyse datasets of high dimensionality and variety, with a pillar of tensor decompositions being the Canonical Polyadic Decomposition (CPD). While the notion of CPD is closely intertwined with that of tensor rank, R unlike the matrix rank, the computation of tensor rank is as NP-hard problem, with an associated computational burden on the CPD. To address this issue, we derive a lower...
Paper Details
Title
Lower Bound on Tensor Rank
Published Date
Sep 12, 2019
Journal
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