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The need for appropriate ways to measure the distance or similarity betweendata is ubiquitous in machine learning, pattern recognition and data mining,but handcrafting such good metrics for specific problems is generallydifficult. This has led to the emergence of metric learning, which aims atautomatically learning a metric from data and has attracted a lot of interestin machine learning and related fields for the past ten years. This surveypaper proposes a systematic review of the metric learning literature,highlighting the pros and cons of each approach. We pay particular attention toMahalanobis distance metric learning, a well-studied and successful framework,but additionally present a wide range of methods that have recently emerged aspowerful alternatives, including nonlinear metric learning, similarity learningand local metric learning. Recent trends and extensions, such assemi-supervised metric learning, metric learning for histogram data and thederivation of generalization guarantees, are also covered. Finally, this surveyaddresses metric learning for structured data, in particular edit distancelearning, and attempts to give an overview of the remaining challenges inmetric learning for the years to come.

A Survey on Metric Learning for Feature Vectors and Structured Data