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Enhanced Similarity Measure for Sparse Subspace Clustering Method

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Advances in Computational Intelligence (IWANN 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10305))

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Abstract

Trying to find clusters in high dimensional data is one of the most challenging issues in machine learning. Within this context, subspace clustering methods have showed interesting results especially when applied in computer vision tasks. The key idea of these methods is to uncover groups of data that are embedding in multiple underlying subspaces. In this spirit, numerous subspace clustering algorithms have been proposed. One of them is Sparse Subspace Clustering (SSC) which has presented notable clustering accuracy. In this paper, the problem of similarity measure used in the affinity matrix construction in the SSC method is discussed. Assessment on motion segmentation and face clustering highlights the increase of the clustering accuracy brought by the enhanced SSC compared to other state-of-the-art subspace clustering methods.

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Authors and Affiliations

  1. LIMTIC Laboratory, Higher Institute of Computer Science of Tunis, University of Tunis EL Manar, Abou Raihane Bayrouni, 2080, Ariana, Tunisia

    Sabra Hechmi, Abir Gallas & Ezzeddine Zagrouba

Authors
  1. Sabra Hechmi
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  2. Abir Gallas
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  3. Ezzeddine Zagrouba
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Corresponding author

Correspondence to Sabra Hechmi .

Editor information

Editors and Affiliations

  1. Universidad de Granada , Granada, Spain

    Ignacio Rojas

  2. University of Malaga , Malaga, Spain

    Gonzalo Joya

  3. Polytechnic University of Catalonia, Vilanova i la Geltrú, Barcelona, Spain

    Andreu Catala

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Hechmi, S., Gallas, A., Zagrouba, E. (2017). Enhanced Similarity Measure for Sparse Subspace Clustering Method. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2017. Lecture Notes in Computer Science(), vol 10305. Springer, Cham. https://doi.org/10.1007/978-3-319-59153-7_26

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  • DOI: https://doi.org/10.1007/978-3-319-59153-7_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59152-0

  • Online ISBN: 978-3-319-59153-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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