eprintid: 1766
rev_number: 11
eprint_status: archive
userid: 12
dir: disk0/00/00/17/66
datestamp: 2016-06-09 02:15:48
lastmod: 2016-06-09 02:15:48
status_changed: 2016-06-09 02:15:48
type: conference_item
metadata_visibility: show
creators_name: Nguyen, Viet Dung
creators_name: Abed-Meraim, Karim
creators_name: Nguyen, Linh Trung
creators_id: linhtrung@vnu.edu.vn
title: Fast adaptive PARAFAC decomposition algorithm with linear complexity
ispublished: pub
subjects: ECE
subjects: ElectronicsandComputerEngineering
divisions: fac_fet
abstract: We present a fast adaptive PARAFAC decomposition algorithm with low computational complexity. The proposed algorithm generalizes the Orthonormal Projection Approximation Subspace Tracking (OPAST) approach for tracking a class of third-order tensors which have one dimension growing with time. It has linear complexity, good convergence rate and good estimation accuracy. To deal with large-scale problems, a parallel implementation can be applied to reduce both computational complexity and storage. We illustrate the effectiveness of our algorithm in comparison with the state-of-the-art algorithms through simulation experiments.
date: 2016-03-20
date_type: published
official_url: http://www.icassp2016.org
full_text_status: public
pres_type: paper
pagerange: 6235-6239
event_title: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
event_location: China
event_dates: 20-25 March 2016
event_type: conference
refereed: TRUE
related_url_url: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7465907
related_url_type: pub
citation:   Nguyen, Viet Dung and Abed-Meraim, Karim and Nguyen, Linh Trung  (2016) Fast adaptive PARAFAC decomposition algorithm with linear complexity.  In: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 20-25 March 2016, China.     
document_url: https://eprints.uet.vnu.edu.vn/eprints/id/eprint/1766/1/IEEE%20Xplore%20Abstract%20-%20Fast%20adaptive%20PARAFAC%20decomposition%20algorithm%20with%20linear%20complexity.pdf