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Classification and Clustering of Spatial Patterns with Geometric Algebra

  • Minh Tuan PhamEmail author
  • Kanta Tachibana
  • Eckhard M. S. Hitzer
  • Tomohiro Yoshikawa
  • Takeshi Furuhashi
Chapter

Abstract

In fields of classification and clustering of patterns most conventional methods of feature extraction do not pay much attention to the geometric properties of data, even in cases where the data have spatial features. This paper proposes to use geometric algebra to systematically extract geometric features from data given in a vector space. We show the results of classification of handwritten digits and those of clustering of consumers’ impression with the proposed method.

Keywords

Feature Extraction Gaussian Mixture Model Expectation Maximization Algorithm Unlabeled Data Kernel Matrix 
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 London 2010

Authors and Affiliations

  • Minh Tuan Pham
    • 1
    Email author
  • Kanta Tachibana
    • 2
  • Eckhard M. S. Hitzer
    • 3
  • Tomohiro Yoshikawa
    • 1
  • Takeshi Furuhashi
    • 1
  1. 1.Nagoya UniversityNagoyaJapan
  2. 2.Kogakuin UniversityTokyoJapan
  3. 3.Department of Applied PhysicsUniversity of FukuiFukuiJapan

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