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

研究成果査読

7 被引用数 (Scopus)

抄録

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.

本文言語English
ページ(範囲)177-190
ページ数14
ジャーナルNeurocomputing
347
DOI
出版ステータスPublished - 2019 6 28
外部発表はい

ASJC Scopus subject areas

  • コンピュータ サイエンスの応用
  • 認知神経科学
  • 人工知能

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