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New robust algorithms for sparse non-negative three-way tensor decompositions

Viet Dung Nguyen and Karim Abed-Meraim and Linh Trung Nguyen (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.

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Official URL: http://dx.doi.org/10.1109/EUSIPCO.2016.7760629

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.

Item Type:Conference or Workshop Item (Lecture)
Subjects:Electronics and Communications
Divisions:Faculty of Electronics and Telecommunications (FET)
ID Code:2193
Deposited By: A/Prof. Linh Trung Nguyen
Deposited On:25 Dec 2016 16:53
Last Modified:25 Dec 2016 16:53

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