A general graph-based semi-supervised learning with novel class discovery

Authors:
Feiping Nie;Shiming Xiang;Yun Liu;Changshui Zhang
Affiliations:
Tsinghua University, State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology, Department of Automation, 100084, Beijing, Chi ...;Chinese Academy of Sciences, National Laboratory of Pattern Recognition, Institute of Automation, 100190, Beijing, China;Nanyang Technological University, School of Electrical & Electronic Engineering, Singapore, Singapore;Tsinghua University, State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology, Department of Automation, 100084, Beijing, Chi ...
Venue:
Neural Computing and Applications
Year:
2010

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Abstract

In this paper, we propose a general graph-based semi-supervised learning algorithm. The core idea of our algorithm is to not only achieve the goal of semi-supervised learning, but also to discover the latent novel class in the data, which may be unlabeled by the user. Based on the normalized weights evaluated on data graph, our algorithm is able to output the probabilities of data points belonging to the labeled classes or the novel class. We also give the theoretical interpretations for the algorithm from three viewpoints on graph, i.e., regularization framework, label propagation, and Markov random walks. Experiments on toy examples and several benchmark datasets illustrate the effectiveness of our algorithm.