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Nonnegative Tensor Train Decompositions for Multi-domain Feature Extraction and Clustering

  • Namgil LeeEmail author
  • Anh-Huy Phan
  • Fengyu Cong
  • Andrzej Cichocki
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9949)

Abstract

Tensor train (TT) is one of the modern tensor decomposition models for low-rank approximation of high-order tensors. For nonnegative multiway array data analysis, we propose a nonnegative TT (NTT) decomposition algorithm for the NTT model and a hybrid model called the NTT-Tucker model. By employing the hierarchical alternating least squares approach, each fiber vector of core tensors is optimized efficiently at each iteration. We compared the performances of the proposed method with a standard nonnegative Tucker decomposition (NTD) algorithm by using benchmark data sets including event-related potential data and facial image data in multi-domain feature extraction and clustering tasks. It is illustrated that the proposed algorithm extracts physically meaningful features with relatively low storage and computational costs compared to the standard NTD model.

Keywords

EEG Feature extraction HALS Tucker decomposition 

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Namgil Lee
    • 1
    Email author
  • Anh-Huy Phan
    • 1
  • Fengyu Cong
    • 2
    • 3
  • Andrzej Cichocki
    • 1
  1. 1.Laboratory for Advanced Brain Signal ProcessingRIKEN Brain Science InstituteWakoJapan
  2. 2.Faculty of Electronic Information and Electrical Engineering, Department of Biomedical EngineeringDalian University of TechnologyDalianChina
  3. 3.Department of Mathematical Information TechnologyUniversity of JyväskyläJyväskyläFinland

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