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Intelligent Data analysis and its Applications, Volume I pp 113–122Cite as

Subspace Learning with Enriched Databases Using Symmetry

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 297))

Abstract

Principal Component Analysis and Linear Discriminant Analysis are of the most known subspace learning techniques. In this paper, a way for training set enrichment is proposed in order to improve the performance of the subspace learning techniques by exploiting the a-priori knowledge that many types of data are symmetric. Experiments on artificial, facial expression recognition, face recognition and object categorization databases denote the robustness of the proposed approach.

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Author information

Authors and Affiliations

  1. Department of Informatics, Aristotle University of Thessaloniki, Box 451, 54124, Thessaloniki, Greece

    Konstantinos Papachristou, Anastasios Tefas & Ioannis Pitas

Authors
  1. Konstantinos Papachristou
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  2. Anastasios Tefas
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  3. Ioannis Pitas
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Corresponding author

Correspondence to Konstantinos Papachristou .

Editor information

Editors and Affiliations

  1. National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan

    Jeng-Shyang Pan

  2. Department of Computer Science Faculty of Elec. Eng. & Comp. Sci., VSB-Technical University of Ostrava, Ostrava-Poruba, Czech Republic

    Vaclav Snasel

  3. Departamento de Informáca y Automática, Facultad de Biología, University of Salamanca, Salamanca, Spain

    Emilio S. Corchado

  4. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, Washington, USA

    Ajith Abraham

  5. Department of Information Management, National University of Kaohsiung, Kaohsiung, Taiwan

    Shyue-Liang Wang

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© 2014 Springer International Publishing Switzerland

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Papachristou, K., Tefas, A., Pitas, I. (2014). Subspace Learning with Enriched Databases Using Symmetry. In: Pan, JS., Snasel, V., Corchado, E., Abraham, A., Wang, SL. (eds) Intelligent Data analysis and its Applications, Volume I. Advances in Intelligent Systems and Computing, vol 297. Springer, Cham. https://doi.org/10.1007/978-3-319-07776-5_13

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  • DOI: https://doi.org/10.1007/978-3-319-07776-5_13

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-07775-8

  • Online ISBN: 978-3-319-07776-5

  • eBook Packages: EngineeringEngineering (R0)

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