Label propagation through linear neighborhoods

  • Authors:
  • Fei Wang;Changshui Zhang

  • Affiliations:
  • Tsinghua University, Beijing, P. R. China;Tsinghua University, Beijing, P. R. China

  • Venue:
  • ICML '06 Proceedings of the 23rd international conference on Machine learning
  • Year:
  • 2006

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Abstract

A novel semi-supervised learning approach is proposed based on a linear neighborhood model, which assumes that each data point can be linearly reconstructed from its neighborhood. Our algorithm, named Linear Neighborhood Propagation (LNP), can propagate the labels from the labeled points to the whole dataset using these linear neighborhoods with sufficient smoothness. We also derive an easy way to extend LNP to out-of-sample data. Promising experimental results are presented for synthetic data, digit and text classification tasks.