Linear Dimensionality Reduction through Eigenvector Selection for Object Recognition

  • F. Dornaika
  • A. Assoum
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6453)


Past work on Linear Dimensionality Reduction (LDR) has emphasized the issues of classification and dimension estimation. However, relatively less attention has been given to the critical issue of eigenvector selection. The main trend in feature extraction has been representing the data in a lower dimensional space, for example, using principal component analysis (PCA) without using an effective scheme to select an appropriate set of features/eigenvectors in this space. This paper addresses Linear Dimensionality Reduction through Eigenvector selection for object recognition. It has two main contributions. First, we propose a unified framework for one transform based LDR. Second, we propose a framework for two transform based DLR. As a case study, we consider PCA and Linear Discriminant Analysis (LDA) for the linear transforms. We have tested our proposed frameworks on several public benchmark data sets. Experiments on ORL, UMIST, and YALE Face Databases and MNIST Handwritten Digit Database show significant performance improvements in recognition that are based on eigenvector selection.


Feature Selection Object Recognition Recognition Rate Feature Subset Feature Selection Algorithm 
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 2010

Authors and Affiliations

  • F. Dornaika
    • 1
    • 2
  • A. Assoum
    • 3
  1. 1.University of the Basque CountrySan SebastianSpain
  2. 2.IKERBASQUEBasque Foundation for ScienceBilbaoSpain
  3. 3.LaMA LaboratoryLebanese UniversityTripoliLebanon

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