Effective Improved Graph Transduction

  title={Effective Improved Graph Transduction},
  author={J. Chen and Y. Zhou and Y. Gao and Bo Wang and Lin-bo Luo and Wenyu Liu},
  journal={J. Softw.},
  • J. Chen, Y. Zhou, +3 authors Wenyu Liu
  • Published 2011
  • Computer Science
  • J. Softw.
  • In this paper, we focus on the problem of shape retrieval and clustering. We put two questions together because they are based on the same method, called Improved Graph Transduction. For shape retrieval, we regard the shape as a node in a graph and the similarity of shapes is represented by the edge of the graph.  Then we learn a new distance measure between the query shape and the testing shapes. The main contribution of our work is to merge the most likely node with the query node during the… CONTINUE READING
    Semi-supervised Tensor Graph-optimized Linear Discriminant Analysis for Two-dimensional Face Recognition
    • S. Xu
    • Computer Science
    • 2013
    • 3
    • PDF


    Publications referenced by this paper.
    Combining labeled and unlabeled data with co-training
    • 4,647
    • PDF
    Learning with Local and Global Consistency
    • 3,438
    • PDF
    Shape Classification Using the Inner-Distance
    • 1,004
    • PDF
    Improving Shape Retrieval by Learning Graph Transduction
    • 149
    • Highly Influential
    • PDF
    Learning from Labeled and Unlabeled Data using Graph Mincuts
    • 989
    • PDF
    Analyzing the effectiveness and applicability of co-training
    • 963
    • PDF