relation: https://eprints.uet.vnu.edu.vn/eprints/id/eprint/2193/
title: New robust algorithms for sparse non-negative three-way tensor decompositions
creator: Nguyen, Viet Dung
creator: Abed-Meraim, Karim
creator: Nguyen, Linh Trung
subject: Electronics and Communications
description: 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
type: Conference or Workshop Item
type: PeerReviewed
format: application/pdf
language: en
identifier: https://eprints.uet.vnu.edu.vn/eprints/id/eprint/2193/1/07760629.pdf
identifier:   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.     
relation: http://dx.doi.org/10.1109/EUSIPCO.2016.7760629