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Informative Laplacian Projection

  • Zhirong Yang
  • Jorma Laaksonen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5575)

Abstract

A new approach of constructing the similarity matrix for eigendecomposition on graph Laplacians is proposed. We first connect the Locality Preserving Projection method to probability density derivatives, which are then replaced by informative score vectors. This change yields a normalization factor and increases the contribution of the data pairs in low-density regions. The proposed method can be applied to both unsupervised and supervised learning. Empirical study on facial images is provided. The experiment results demonstrate that our method is advantageous for discovering statistical patterns in sparse data areas.

Keywords

Face Recognition Linear Discriminant Analysis Facial Image Locality Preserve Projection Nonlinear Dimensionality Reduction 
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 2009

Authors and Affiliations

  • Zhirong Yang
    • 1
  • Jorma Laaksonen
    • 1
  1. 1.Department of Information and Computer ScienceHelsinki University of TechnologyTKK, EspooFinland

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