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Laplacian Discriminant Projection Based on Affinity Propagation

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Artificial Intelligence and Computational Intelligence (AICI 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5855))

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Abstract

The paper proposes a new algorithm for supervised dimensionality reduction, called Laplacian Discriminant Projection based on Affinity Propagation (APLDP). APLDP defines three scatter matrices using similarities based on representative exemplars which are found by Affinity Propagation Clustering. After linear transformation, the considered pairwise samples within the same exemplar subset and the same class are as close as possible, while those exemplars between classes are as far as possible. The experiments on several data sets demonstrate the competence of APLDP.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Zhejiang Normal University, Zhejiang, China

    Xueping Chang & Zhonglong Zheng

Authors
  1. Xueping Chang
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  2. Zhonglong Zheng
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Editor information

Editors and Affiliations

  1. School of Business Information Technology, RMIT University, City Campus, 124 La Trobe Street, 3000, Melbourne, Victoria, Australia

    Hepu Deng

  2. College of Metrological Technology and Engineering, China Jiliang University, 310018, Hangzhou, Zhejiang Province, China

    Lanzhou Wang

  3. Department of Computer Science, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon Tong, Hong Kong, China

    Fu Lee Wang

  4. College of Information Science and Technology, Hainan University, 570228, Haikou, China

    Jingsheng Lei

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© 2009 Springer-Verlag Berlin Heidelberg

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Cite this paper

Chang, X., Zheng, Z. (2009). Laplacian Discriminant Projection Based on Affinity Propagation. In: Deng, H., Wang, L., Wang, F.L., Lei, J. (eds) Artificial Intelligence and Computational Intelligence. AICI 2009. Lecture Notes in Computer Science(), vol 5855. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05253-8_35

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  • DOI: https://doi.org/10.1007/978-3-642-05253-8_35

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-05252-1

  • Online ISBN: 978-3-642-05253-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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