This paper surveys the field of distance metric learning from a principle perspective, and includes a broad selection of recent work. In particular, distance metric learning is reviewed under different learning conditions: supervised learning versus unsupervised learning, learning in a global sense versus in a local sense and the distance matrix based on linear kernel versus nonlinear kernel. In addition, this paper discusses a number of techniques that is central to distance metric learning, including convex programming, positive semi-definite programming, kernel learning, dimension reduction, K Nearest Neighbor, large margin classification, and graph-based approaches.

metric learning_survey_v2.pdf

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