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A Two Stage Algorithm for K-Mode Convolutive Nonnegative Tucker Decomposition

  • Qiang Wu
  • Liqing Zhang
  • Andrzej Cichocki
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7063)

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.

Keywords

Speaker Recognition Tensor Factorization Sparse Constraint Noisy Condition Speaker Modeling 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Qiang Wu
    • 1
  • Liqing Zhang
    • 2
  • Andrzej Cichocki
    • 3
  1. 1.School of Information Science and EngineeringShandong UniversityJinanChina
  2. 2.Department of Computer Science and EngineeringShanghai Jiao Tong UniversityShanghaiChina
  3. 3.Laboratory for Advanced Brain Signal ProcessingBSI RIKENWakoshiJapan

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