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Similarity-Based Sparse Feature Extraction Using Local Manifold Learning

  • Cheong Hee Park
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3918)

Abstract

Feature extraction is an important preprocessing step which is encountered in many areas such as data mining, pattern recognition and scientific visualization. In this paper, a new method for sparse feature extraction using local manifold learning is proposed. Similarities in a neighborhood are first computed to explore local geometric structures, producing sparse feature representation. Based on the constructed similarity matrix, linear dimension reduction is applied to enhance similarities among the elements in the same class and extract optimal features for classification performances. Since it only computes similarities in a neighborhood, sparsity in the similarity matrix can give computational efficiency and memory savings. Experimental results demonstrate superior performances of the proposed method.

Keywords

Linear Discriminant Analysis Similarity Matrix Nonlinear Dimensionality Reduction Sparse Feature Local Manifold 
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 2006

Authors and Affiliations

  • Cheong Hee Park
    • 1
  1. 1.Dept. of Computer Science and EngineeringChungnam National UniversityDaejeonKorea

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