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Vol. 177, Issue 8, August 2014, pp. 238-245

 

Bullet

 

Based on Similarity Metric Learning for Semi-Supervised Clustering
 

Wei QIU

School of Computer Science, Jiaying University, Meizhou, Guangdong, 514015, China
Tel.: 13421003080, fax: 2186960

E-mail: qiuwei@jyu.edu.cn

 

Received: 21 May 2014 /Accepted: 31 July 2014 /Published: 31 August 2014

Digital Sensors and Sensor Sysstems

 

Abstract: Semi-supervised clustering employs a small amount of labeled data to aid unsupervised learning. The focus of this paper is on Metric Learning, with particular interest in incorporating side information to make it semi-supervised. This study is primarily motivated by an application: face-image clustering. In the paper introduces metric learning and semi-supervised clustering, Similarity metric learning method that adapt the underlying similarity metric used by the clustering algorithm. This paper provides new methods for the two approaches as well as presents a new semi-supervised clustering algorithm that integrates both of these techniques in a uniform, principled framework. Experimental results demonstrate that the unified approach produces better clusters than both individual approaches as well as previously proposed semi-supervised clustering algorithms. This paper followed by the discussion of experiments on face-image clustering, as well as future work.

 

Keywords: Semi-supervised clustering, Clustering algorithm, Similarity metric learning.

 

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