关于metric learning的综述，涉及到许多的知识：SVM、kernel、SDP等-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.