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Neural Computing and Applications

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01-06-2010 | Original Article

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

Published in: Neural Computing and Applications | Issue 4/2010

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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.

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Title: A general graph-based semi-supervised learning with novel class discovery
Publication date: 01-06-2010
Published in: Neural Computing and Applications / Issue 4/2010
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-009-0305-8

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