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2010 Second WRI Global Congress on Intelligent Systems
Metric learning performs a task of constructing a metric space that reflects relationship of training data. Both supervised and semi-supervised settings are well studied. In this paper, we propose a method to perform semi-supervised classification in a metric learning setting. The proposed method is based on non-metric Multi-Dimensional Scaling (NMDS). An original metric space is generated using labeled data by NMDS. Unlabeled data is added to this metric space and an updated procedure is useddoi:10.1109/gcis.2010.223 fatcat:45bnryknzfaw7ecfwzbsnd7anm