Based on Similarity Metric Learning for Semi-Supervised Clustering

2014 Sensors & Transducers  
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
more » ... 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.
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