Reliable Semi-supervised Learning

In this paper, we propose a Reliable Semi-Supervised Learning framework, called ReSSL, for both static and streaming data. Instead of relaxing different assumptions, we do model the reliability of cluster assumption, quantify the distinct importance of clusters (or evolving micro-clusters on data st...

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Bibliographic Details
Main Authors: Junming Shao, Chen Huang, Qinli Yang, Guangchun Luo
Format: Conference Proceeding
Language:English
Subjects:
Clustering algorithms
Data models
data stream
Particle separators
Reliability
semi-supervised learning
Semisupervised learning
Supervised learning
Training data
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Summary:In this paper, we propose a Reliable Semi-Supervised Learning framework, called ReSSL, for both static and streaming data. Instead of relaxing different assumptions, we do model the reliability of cluster assumption, quantify the distinct importance of clusters (or evolving micro-clusters on data streams), and integrate the cluster-level information and labeled data for prediction with a lazy learning framework. Extensive experiments demonstrate that our method has good performance compared to state-of-the-art algorithms on data sets in both static and real streaming environments.
ISSN:2374-8486
DOI:10.1109/ICDM.2016.0159