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Semi-supervised Learning Based on Label Propagation through Submanifold

  • Jiani Hu
  • Weihong Deng
  • Jun Guo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5551)

Abstract

A semi-supervised learning algorithm is proposed based on label propagation through submanifold. The algorithm assumes that samples lying in a local neighborhood share the same labels and the global labels changing among submanifolds is sufficiently smooth. The algorithm firstly introduces a k-nearest neighbor graph to describe local neighborhood among the data set. And then, a cost function and a constraint equation are proposed, which stand for the global smoothness of the class labels’ changing and the labeled samples’ information respectively. The final semi-supervised learning task is converted to a typical quadratic program, whose optimal solution can minimize the cost function and satisfy the supervised constraint. Experimental results of the algorithm on toy data, digit recognition, and text classification demonstrate the feasibility and efficiency of the proposed algorithm.

Keywords

Semi-supervised learning K-nearest neighbor graph Quadratic program Classification 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Jiani Hu
    • 1
  • Weihong Deng
    • 1
  • Jun Guo
    • 1
  1. 1.Beijing University of Posts and TelecommunicationsBeijingChina

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