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Local Structure Preserving Based Subspace Analysis Methods and Applications

  • Chapter
Subspace Methods for Pattern Recognition in Intelligent Environment

Part of the book series: Studies in Computational Intelligence ((SCI,volume 552))

Abstract

Subspace analysis is an effective approach for image representation. Local structure preserving has been widely adopted to learn subspace which reflects the intrinsic attributes of samples. In this chapter, inspired by the idea of local structure preserving, we propose two novel subspace methods for face recognition and image clustering tasks. The first is named Supervised Kernel Locality Preserving Projections (SKLPP) for face recognition task, in which geometric relations are preserved according to prior class-label information and complex nonlinear variations of real face images are represented by nonlinear kernel mapping. The second is a novel probabilistic topic model for image clustering task, named Dual Local Consistency Probabilistic Latent Semantic Analysis (DLC-PLSA), The proposed DLC-PLSA model can learn an effective and robust mid-level representation in the latent semantic space for image analysis. As our model considers both the local image structure and local word consistency simultaneously when estimating the probabilistic topic distributions, the image representations can have more powerful description ability in the learned latent semantic space. The extensive experiments on face recognition and image clustering show that the proposed subspace analysis methods are promising.

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Authors and Affiliations

  1. National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Jian Cheng & Hanqing Lu

Authors
  1. Jian Cheng
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  2. Hanqing Lu
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Correspondence to Jian Cheng .

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Editors and Affiliations

  1. Ritsumeikan University College of Science & Engineering, Kusuatsu, Shiga, Japan

    Yen-Wei Chen

  2. Faculty of Education, Science, Technology & Mathematics, University of Canberra, Canberra, South Australia, Australia

    Lakhmi C. Jain

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Cheng, J., Lu, H. (2014). Local Structure Preserving Based Subspace Analysis Methods and Applications. In: Chen, YW., C. Jain, L. (eds) Subspace Methods for Pattern Recognition in Intelligent Environment. Studies in Computational Intelligence, vol 552. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54851-2_5

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  • DOI: https://doi.org/10.1007/978-3-642-54851-2_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-54850-5

  • Online ISBN: 978-3-642-54851-2

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