Online low-rank tensor subspace tracking from incomplete data by CP decomposition using recursive least squares

Hiroyuki Kasai

ICASSP 2016accepted0 citations

Online low-rank tensor subspace tracking from incomplete data by CP decomposition using recursive least squares

Abstract

We propose an online tensor subspace tracking algorithm based on the CP decomposition exploiting the recursive least squares (RLS), dubbed OnLine Low-rank Subspace tracking by TEnsor CP Decomposition (OLSTEC). Numerical evaluations show that the proposed OLSTEC algorithm gives faster convergence per iteration comparing with the state-of-the-art online algorithms.

BibTeX

@inproceedings{icassp2016_onlinelowrankten,
  title = {Online low-rank tensor subspace tracking from incomplete data by CP decomposition using recursive least squares},
  author = {Hiroyuki Kasai},
  booktitle = {ICASSP 2016},
  year = {2016}
}