Evolutionary Distance Metric Learning Approach to Semi-supervised Clustering with Neighbor Relations
Ken–ichi Fukui, Satoshi Ono, Taishi Megano, Masayuki Numao · 2013
This study proposes a distance metric learning method based on a clustering index with neighbor relation that simultaneously evaluates inter-and intra-clusters. Our proposed method optimizes a distance transform matrix based on the Mahalanobis distance by utilizing a self-adaptive differential evolution (jDE) algorithm. Our approach directly improves various clustering indices and in principle requires less auxiliary information compared to conventional metric learning methods. We experimentally validated the search efficiency of jDE and the generalization performance.