ReLISH: Reliable Label Inference via Smoothness Hypothesis

Authors

  • Chen Gong Shanghai Jiao Tong University and University of Technology Sydney
  • Dacheng Tao University of Technology Sydney
  • Keren Fu Shanghai Jiao Tong University
  • Jie Yang Shanghai Jiao Tong University

DOI:

https://doi.org/10.1609/aaai.v28i1.8955

Keywords:

Semi-supervised learning, Local smoothness, Regularization

Abstract

The smoothness hypothesis is critical for graph-based semi-supervised learning. This paper defines local smoothness, based on which a new algorithm, Reliable Label Inference via Smoothness Hypothesis (ReLISH), is proposed. ReLISH has produced smoother labels than some existing methods for both labeled and unlabeled examples. Theoretical analyses demonstrate good stability and generalizability of ReLISH. Using real-world datasets, our empirical analyses reveal that ReLISH is promising for both transductive and inductive tasks, when compared with representative algorithms, including Harmonic Functions, Local and Global Consistency, Constraint Metric Learning, Linear Neighborhood Propagation, and Manifold Regularization.

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Published

2014-06-21

How to Cite

Gong, C., Tao, D., Fu, K., & Yang, J. (2014). ReLISH: Reliable Label Inference via Smoothness Hypothesis. Proceedings of the AAAI Conference on Artificial Intelligence, 28(1). https://doi.org/10.1609/aaai.v28i1.8955

Issue

Section

Main Track: Novel Machine Learning Algorithms