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

Hiroyuki Kasai

doi:10.1109/ICASSP.2016.7472131

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

Hiroyuki Kasai^*

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

30 Citations (Scopus)

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.

Original language	English
Title of host publication	2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2519-2523
Number of pages	5
ISBN (Electronic)	9781479999880
DOIs	https://doi.org/10.1109/ICASSP.2016.7472131
Publication status	Published - 2016 May 18
Externally published	Yes
Event	41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Shanghai, China Duration: 2016 Mar 20 → 2016 Mar 25

Publication series

Name	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume	2016-May
ISSN (Print)	1520-6149

Other

Other	41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016
Country/Territory	China
City	Shanghai
Period	16/3/20 → 16/3/25

Keywords

CP decomposition
Online subspace tracking
Recursive least squares
Tensor completion

ASJC Scopus subject areas

Software
Signal Processing
Electrical and Electronic Engineering

Access to Document

10.1109/ICASSP.2016.7472131

Cite this

Kasai, H. (2016). Online low-rank tensor subspace tracking from incomplete data by CP decomposition using recursive least squares. In 2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings (pp. 2519-2523). [7472131] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2016-May). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2016.7472131

Online low-rank tensor subspace tracking from incomplete data by CP decomposition using recursive least squares. / Kasai, Hiroyuki.

2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2016. p. 2519-2523 7472131 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2016-May).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Kasai, H 2016, Online low-rank tensor subspace tracking from incomplete data by CP decomposition using recursive least squares. in 2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings., 7472131, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, vol. 2016-May, Institute of Electrical and Electronics Engineers Inc., pp. 2519-2523, 41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016, Shanghai, China, 16/3/20. https://doi.org/10.1109/ICASSP.2016.7472131

Kasai H. Online low-rank tensor subspace tracking from incomplete data by CP decomposition using recursive least squares. In 2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2016. p. 2519-2523. 7472131. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). doi: 10.1109/ICASSP.2016.7472131

Kasai, Hiroyuki. / Online low-rank tensor subspace tracking from incomplete data by CP decomposition using recursive least squares. 2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 2519-2523 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings).

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

Abstract

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Keywords

ASJC Scopus subject areas

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