%A Viet Dung Nguyen
%A Karim Abed-Meraim
%A Linh Trung Nguyen
%T New robust algorithms for sparse non-negative three-way tensor decompositions
%X 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.
%C Budapest, Hungary
%D 2016
%P 2151-2155
%L SisLab2193