• DocumentCode
    148980
  • Title

    On the need for metrics in dictionary learning assessment

  • Author

    Chevallier, Sylvain ; Barthelemy, Quentin ; Atif, Jamal

  • Author_Institution
    LISV, Univ. of Versailles, Versailles, France
  • fYear
    2014
  • fDate
    1-5 Sept. 2014
  • Firstpage
    1427
  • Lastpage
    1431
  • Abstract
    Dictionary-based approaches are the focus of a growing attention in the signal processing community, often achieving state of the art results in several application fields. Albeit their success, the criteria introduced so far for the assessment of their performances suffer from several shortcomings. The scope of this paper is to conduct a thorough analysis of these criteria and to highlight the need for principled criteria, enjoying the properties of metrics. Henceforth we introduce new criteria based on transportation like metrics and discuss their behaviors w.r.t the literature.
  • Keywords
    learning (artificial intelligence); signal processing; dictionary learning assessment; signal processing community; Atomic measurements; Convergence; Dictionaries; Signal to noise ratio; Training; Transportation; Dictionary learning; detection rate; dictionary recovering; metric; transportation distance;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Signal Processing Conference (EUSIPCO), 2014 Proceedings of the 22nd European
  • Conference_Location
    Lisbon
  • Type

    conf

  • Filename
    6952505