Semi-Supervised Classification with Universum

9 years 2 months ago
Semi-Supervised Classification with Universum
The Universum data, defined as a collection of "nonexamples" that do not belong to any class of interest, have been shown to encode some prior knowledge by representing meaningful concepts in the same domain as the problem at hand. In this paper, we address a novel semi-supervised classification problem, called semi-supervised Universum, that can simultaneously utilize the labeled data, unlabeled data and the Universum data to improve the classification performance. We propose a graph based method to make use of the Universum data to help depict the prior information for possible classifiers. Like conventional graph based semi-supervised methods, the graph regularization is also utilized to favor the consistency between the labels. Furthermore, since the proposed method is a graph based one, it can be easily extended to the multiclass case. The empirical experiments on the USPS and MNIST datasets are presented to show that the proposed method can obtain superior performances...
Dan Zhang, Jingdong Wang, Fei Wang, Changshui Zhan
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2008
Where SDM
Authors Dan Zhang, Jingdong Wang, Fei Wang, Changshui Zhang
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