eprintid: 2193
rev_number: 6
eprint_status: archive
userid: 17
dir: disk0/00/00/21/93
datestamp: 2016-12-25 16:53:55
lastmod: 2016-12-25 16:53:55
status_changed: 2016-12-25 16:53:55
type: conference_item
metadata_visibility: show
creators_name: Nguyen, Viet Dung
creators_name: Abed-Meraim, Karim
creators_name: Nguyen, Linh Trung
title: New robust algorithms for sparse non-negative three-way tensor decompositions
ispublished: pub
subjects: ECE
divisions: fac_fet
abstract: Tensor decomposition is an important tool for many applications in diverse disciplines such as signal processing, chemometrics, numerical linear algebra and data mining. In this work, we focus on PARAFAC and Tucker decompositions of three-way tensors with non-negativity and/or sparseness constraints. By using an all-at-once optimization approach, we propose two decomposition algorithms which are robust to tensor order over-estimation errors, – a desired practical property when the tensor rank is unknown. Different algorithm versions are proposed depending on the desired constraint (or property) of the tensor factors or the core tensor. Finally, the performance of the algorithms are assessed via insightful simulation experiments on both simulated and real-life data.
date: 2016
date_type: published
official_url: http://dx.doi.org/10.1109/EUSIPCO.2016.7760629
full_text_status: restricted
pres_type: lecture
place_of_pub: Budapest, Hungary
pagerange: 2151-2155
event_title: 24th European Signal Processing Conference (EUSIPCO)
event_location: Budapest, Hungary
event_dates: 29 August - 2 September
event_type: conference
refereed: TRUE
citation:   Nguyen, Viet Dung and Abed-Meraim, Karim and Nguyen, Linh Trung  (2016) New robust algorithms for sparse non-negative three-way tensor decompositions.  In: 24th European Signal Processing Conference (EUSIPCO), 29 August - 2 September, Budapest, Hungary.     
document_url: https://eprints.uet.vnu.edu.vn/eprints/id/eprint/2193/1/07760629.pdf