Fast online low-rank tensor subspace tracking by CP decomposition using recursive least squares from incomplete observations

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

This paper considers the problem of online tensor subspace tracking of a partially observed high-dimensional data stream corrupted by noise, where we assume that the data lie in a low-dimensional linear subspace. This problem is cast as an online low-rank tensor completion problem. We propose a novel online tensor subspace tracking algorithm based on the CANDECOMP/PARAFAC (CP) decomposition, dubbed OnLine Low-rank Subspace tracking by TEnsor CP Decomposition (OLSTEC). The proposed algorithm specifically addresses the case in which data of interest are fed into the algorithm over time infinitely, and their subspace are dynamically time-varying. To this end, we build up our proposed algorithm exploiting the recursive least squares (RLS), which is a second-order gradient algorithm. Numerical evaluations on synthetic datasets and real-world datasets such as communication network traffic, environmental data, and surveillance videos, show that the proposed OLSTEC algorithm outperforms state-of-the-art online algorithms in terms of the convergence rate per iteration.

Original languageEnglish
Pages (from-to)177-190
Number of pages14
JournalNeurocomputing
Volume347
DOIs
Publication statusPublished - 2019 Jun 28
Externally publishedYes

Keywords

  • Low-rank tensor completion
  • Online subspace tracking
  • Recursive least squares

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

Fingerprint Dive into the research topics of 'Fast online low-rank tensor subspace tracking by CP decomposition using recursive least squares from incomplete observations'. Together they form a unique fingerprint.

  • Cite this