Export file:

Format

  • RIS(for EndNote,Reference Manager,ProCite)
  • BibTex
  • Text

Content

  • Citation Only
  • Citation and Abstract

On identifiability of 3-tensors of multilinear rank (1; Lr; Lr)

1. Department of Computer Science Southern Illinois University-Carbondale Carbondale, IL 62901, USA;
2. Department of Computer Science Southern Illinois University-Carbondale Carbondale, IL 62901, USA;
3. Department of Mathematics Lamar University Beaumont, TX 77710, USA;
4. Department of Mathematics Southern Illinois University-Carbondale Carbondale, IL 62901, USA

In this paper, we study a specific big data model via multilinear rank tensor decompositions. The model approximates to a given tensor by the sum of multilinear rank (1; Lr; Lr) terms. And we characterize the identifiability property of this model from a geometric point of view. Our main results consists of exact identifiability and generic identifiability. The arguments of generic identifiability relies on the exact identifiability, which is in particular closely related to the well-known "trisecant lemma" in the context of algebraic geometry (see Proposition 2.6 in[1]). This connection discussed in this paper demonstrates a clear geometric picture of this model.
  Figure/Table
  Supplementary
  Article Metrics
Download full text in PDF

Export Citation

Article outline

Copyright © AIMS Press All Rights Reserved