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On the Behavior of Kernel Mutual Subspace Method

  • Hitoshi Sakano
  • Osamu Yamaguchi
  • Tomokazu Kawahara
  • Seiji Hotta
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6469)

Abstract

Optimizing the parameters of kernel methods is an unsolved problem. We report an experimental evaluation and a consideration of the parameter dependences of kernel mutual subspace method (KMS). The following KMS parameters are considered: Gaussian kernel parameters, the dimensionalities of dictionary and input subspaces, and the number of canonical angles. We evaluate the recognition accuracies of KMS through experiments performed using the ETH-80 animal database. By searching exhaustively for optimal parameters, we obtain 100% recognition accuracy, and some experimental results suggest relationships between the dimensionality of subspaces and the degrees of freedom for the motion of objects. Such results imply that KMS achieves a high recognition rate for object recognition with optimized parameters.

Keywords

Training Sample Object Recognition Recognition Rate Recognition Accuracy Kernel Parameter 
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

  • Hitoshi Sakano
    • 1
  • Osamu Yamaguchi
    • 2
  • Tomokazu Kawahara
    • 3
  • Seiji Hotta
    • 4
  1. 1.NTT Communication Science Lab.Cityh KyotoJapan
  2. 2.Power and Industrial System R&D CenterToshiba Corporation Power Systems CompanyTokyoJapan
  3. 3.Corporate R&D CenterToshiba CorporationKawasakiJapan
  4. 4.The Graduate School of EngineeringTokyo University of Agriculture and TechnologyTokyoJapan

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