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A general graph-based semi-supervised learning with novel class discovery

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Abstract

In this paper, we propose a general graph-based semi-supervised learning algorithm. The core idea of our algorithm is to not only achieve the goal of semi-supervised learning, but also to discover the latent novel class in the data, which may be unlabeled by the user. Based on the normalized weights evaluated on data graph, our algorithm is able to output the probabilities of data points belonging to the labeled classes or the novel class. We also give the theoretical interpretations for the algorithm from three viewpoints on graph, i.e., regularization framework, label propagation, and Markov random walks. Experiments on toy examples and several benchmark datasets illustrate the effectiveness of our algorithm.

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Authors and Affiliations

  1. State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology, Department of Automation, Tsinghua University, Beijing, 100084, China

    Feiping Nie & Changshui Zhang

  2. National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

    Shiming Xiang

  3. School of Electrical & Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Yun Liu

Authors
  1. Feiping Nie
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  2. Shiming Xiang
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  3. Yun Liu
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  4. Changshui Zhang
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Correspondence to Feiping Nie.

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Nie, F., Xiang, S., Liu, Y. et al. A general graph-based semi-supervised learning with novel class discovery. Neural Comput & Applic 19, 549–555 (2010). https://doi.org/10.1007/s00521-009-0305-8

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  • DOI: https://doi.org/10.1007/s00521-009-0305-8

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