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Incremental maximum margin clustering

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Abstract

This paper proposes incremental maximum margin clustering in which one data point at a time is examined to decide which cluster the new data point belongs. The proposed method adopts the off-line iterative maximum margin clustering method’s alternating optimization algorithm. Accurate online support vector regression is employed in the alternating optimization. To avoid premature convergence, a sequence of decremental unlearning and incremental learning steps is performed. The proposed method is experimentally argued to (i) be scalable and competitive on training time front when compared with iterative maximum margin clustering and (ii) achieve competitive cluster quality compared to the off-line counterpart.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Technology Guwahati, Guwahati, India

    V. Vijaya Saradhi

  2. Oracle Development Center, Bengaluru, India

    P. Charly Abraham

Authors
  1. V. Vijaya Saradhi
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  2. P. Charly Abraham
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Correspondence to V. Vijaya Saradhi.

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Vijaya Saradhi, V., Charly Abraham, P. Incremental maximum margin clustering. Pattern Anal Applic 19, 1057–1067 (2016). https://doi.org/10.1007/s10044-015-0447-5

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  • DOI: https://doi.org/10.1007/s10044-015-0447-5

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