A two stage algorithm for K-mode convolutive nonnegative tucker decomposition

Authors:
Qiang Wu;Liqing Zhang;Andrzej Cichocki
Affiliations:
School of Information Science and Engineering, Shandong University, Jinan, Shandong, China;Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China;Laboratory for Advanced Brain Signal Processing, BSI RIKEN, Wakoshi, Saitama, Japan
Venue:
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
Year:
2011

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Abstract

Higher order tensor model has been seen as a potential mathematical framework to manipulate the multiple factors underlying the observations. In this paper, we propose a flexible two stage algorithm for K-mode Convolutive Nonnegative Tucker Decomposition (K-CNTD) model by an alternating least square procedure. This model can be seen as a convolutive extension of Nonnegative Tucker Decomposition (NTD). Shift-invariant features in different subspaces can be extracted by the K-CNTD algorithm. We impose additional sparseness constraint on the algorithm to find the part-based representations. Extensive simulation results indicate that the K-CNTD algorithm is efficient and provides good performance for a feature extraction task.