Robust simultaneous low rank approximation of tensors

Kohei Inoue, Kenji Hara, Kiichi Urahama

    Research output: Contribution to journalConference articlepeer-review

    2 Citations (Scopus)


    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.

    Original languageEnglish
    Pages (from-to)574-584
    Number of pages11
    JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume5414 LNCS
    Publication statusPublished - 2009
    Event3rd Pacific Rim Symposium on Image and Video Technology, PSIVT 2009 - Tokyo, Japan
    Duration: Jan 13 2009Jan 16 2009

    All Science Journal Classification (ASJC) codes

    • Theoretical Computer Science
    • General Computer Science


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