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Discriminant Projections Embedding for Nearest Neighbor Classification

  • Petia Radeva
  • Jordi Vitrià
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3287)

Abstract

In this paper we introduce a new embedding technique to linearly project labeled data samples into a new space where the performance of a Nearest Neighbor classifier is improved. The approach is based on considering a large set of simple discriminant projections and finding the subset with higher classification performance. In order to implement the feature selection process we propose the use of the adaboost algorithm. The performance of this technique is tested in a multiclass classification problem related to the production of cork stoppers for wine bottles.

Keywords

Linear Discriminant Analysis Scatter Matrix Adaboost Algorithm Feature Extraction Process Feature Selection Process 
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 2004

Authors and Affiliations

  • Petia Radeva
    • 1
  • Jordi Vitrià
    • 1
  1. 1.Computer Vision Centre and Dept. InformàticaUniversitat Autònoma de BarcelonaBellaterra (Barcelona)Spain

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