Distance Metric Learning Through Convex Optimization
نویسنده
چکیده
We present a survey of recent work on the problem of learning a distance metric in the framework of semidefinite programming (SDP). Along with a brief theoretical background on convex optimization and distance metrics, we present various methods developed in this context under different approaches and provide theoretical analysis for a subset of them. A gradient ascent projection algorithm (Xing, 2002) and an approximate Frank-Wolfe method (Ying, 2012) are implemented and tested on several standard classification tasks from machine learning. We provide a comparison of the results obtained by our implementations, along with the corresponding results for some state-of-the-art algorithms.
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