Optimal Component Analysis (OCA) is a linear subspace technique for dimensionality reduction designed to optimize object classification and recognition performance. The linear nature of OCA often limits recognition performance, if the underlying data structure is nonlinear or cluster structures are complex. To address these problems, we investigate a kernel analogue of OCA, which consists of applying OCA techniques to the data after it has been mapped nonlinearly into a new feature space, typically a high (possibly infinite) dimensional Hilbert space. In this paper, we study both the theoretical and algorithmic aspects of the problem and report results obtained in several object recognition experiments.


Kernel Function Recognition Rate Performance Function Gaussian Kernel Recognition Performance 
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© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Xiuwen Liu
    • 1
  • Washington Mio
    • 2
  1. 1.Department of Computer ScienceFlorida State UniversityTallahassee
  2. 2.Department of MathematicsFlorida State UniversityTallahasseeUSA

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