Linear Neighborhood Propagation and Its Applications

  • Authors:

  • Jingdong Wang;Fei Wang;Changshui Zhang;Helen C. Shen;Long Quan

  • Affiliations:

  • Microsoft Research Asia, Beijing;Tsinghua University, Beijing;Tsinghua University, Beijing;The Hong Kong University of Science and Technology, Hong Kong;The Hong Kong University of Science and Technology, Hong Kong

  • Venue:
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Year:
  • 2009

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Abstract

In this paper, a novel graph-based transductive classification approach, called Linear Neighborhood Propagation, is proposed. The basic idea is to predict the label of a data point according to its neighbors in a linear way. This method can be cast into a second-order intrinsic Gaussian Markov random field framework. Its result corresponds to a solution to an approximate inhomogeneous biharmonic equation with Dirichlet boundary conditions. Different from existing approaches, our approach provides a novel graph structure construction method by introducing multiple-wise edges instead of pairwise edges, and presents an effective scheme to estimate the weights for such multiple-wise edges. To the best of our knowledge, these two contributions are novel for semi-supervised classification. The experimental results on image segmentation and transductive classification demonstrate the effectiveness and efficiency of the proposed approach.