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.
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Wu, Q., Zhang, L., Cichocki, A. (2011). A Two Stage Algorithm for K-Mode Convolutive Nonnegative Tucker Decomposition. In: Lu, BL., Zhang, L., Kwok, J. (eds) Neural Information Processing. ICONIP 2011. Lecture Notes in Computer Science, vol 7063. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24958-7_77
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DOI: https://doi.org/10.1007/978-3-642-24958-7_77
Publisher Name: Springer, Berlin, Heidelberg
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