Transductive Learning on Adaptive Graphs

Authors

  • Yan-Ming Zhang Chinese Academy of Sciences
  • Yu Zhang Hong Kong University of Science and Technology
  • Dit-Yan Yeung Hong Kong University of Science and Technology
  • Cheng-Lin Liu Chinese Academy of Sciences
  • Xinwen Hou Chinese Academy of Sciences

DOI:

https://doi.org/10.1609/aaai.v24i1.7670

Keywords:

semi-supervised learning, transductive learning, classification

Abstract

Graph-based semi-supervised learning methods are based on some smoothness assumption about the data. As a discrete approximation of the data manifold, the graph plays a crucial role in the success of such graph-based methods. In most existing methods, graph construction makes use of a predefined weighting function without utilizing label information even when it is available. In this work, by incorporating label information, we seek to enhance the performance of graph-based semi-supervised learning by learning the graph and label inference simultaneously. In particular, we consider a particular setting of semi-supervised learning called transductive learning. Using the LogDet divergence to define the objective function, we propose an iterative algorithm to solve the optimization problem which has closed-form solution in each step. We perform experiments on both synthetic and real data to demonstrate improvement in the graph and in terms of classification accuracy.

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Published

2010-07-03

How to Cite

Zhang, Y.-M., Zhang, Y., Yeung, D.-Y., Liu, C.-L., & Hou, X. (2010). Transductive Learning on Adaptive Graphs. Proceedings of the AAAI Conference on Artificial Intelligence, 24(1), 661-666. https://doi.org/10.1609/aaai.v24i1.7670