Semi-supervised classification using multiple clusterings

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• 作者：G. X. Yu ; L. Feng ; G. J. Yao ; J. Wang
• 关键词：semi ; supervised classification ; multiple clusterings ; composite graph
• 刊名：Pattern Recognition and Image Analysis
• 出版年：2016
• 出版时间：October 2016
• 年：2016
• 卷：26
• 期：4
• 页码：681-687
• 全文大小：
• 刊物类别：Computer Science
• 刊物主题：Pattern Recognition; Image Processing and Computer Vision;
• 出版者：Pleiades Publishing
• ISSN：1555-6212
• 卷排序：26

文摘

Graph determines the performance of graph-based semi-supervised classification. In this paper, we investigate how to construct a graph from multiple clusterings and propose a method called Semi-Supervised Classification using Multiple Clusterings (SSCMC in short). SSCMC firstly projects original samples into different random subspaces and performs clustering on the projected samples. Then, it constructs a graph by setting an edge between two samples if these two samples are clustered in the same cluster for each clustering. Next, it combines these graphs into a composite graph and incorporates the resulting composite graph with a graph-based semi-supervised classifier based on local and global consistency. Our experimental results on two publicly available facial images show that SSCMC not only achieves higher accuracy than other related methods, but also is robust to input parameters.