Robust Simultaneous Low Rank Approximation of Tensors

  • Kohei Inoue
  • Kenji Hara
  • Kiichi Urahama
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5414)


We propose simultaneous low rank approximation of tensors (SLRAT) for the dimensionality reduction of tensors and modify it to the robust one, i.e., the robust SLRAT. For both the SLRAT and the robust SLRAT, we propose iterative algorithms for solving them. It is experimentally shown that the robust SLRAT achieves lower reconstruction error than the SLRAT when a dataset contains noise data. We also propose a method for classifying sets of tensors and call it the subspace matching, where both training data and testing data are represented by their subspaces, and each testing datum is classified on the basis of the similarity between subspaces. It is experimentally verified that the robust SLRAT achieves higher recognition rate than the SLRAT when the testing data contain noise data.


Root Mean Square Error Recognition Rate Face Image Noise Image High Recognition Rate 
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 2009

Authors and Affiliations

  • Kohei Inoue
    • 1
  • Kenji Hara
    • 1
  • Kiichi Urahama
    • 1
  1. 1.Kyushu UniversityFukuokaJapan

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