document: Transfer metric learning by learning task relationships Distance metric learning plays a very crucial role in many data mining algorithms because the performance of an algorithm relies heavily on choosing a good metric . However , the labeled data available in many applications is scarce and hence the metrics learned are often unsatisfactory . In this paper , we consider a transfer learning setting in which some related source tasks with labeled data are available to help the learning of the target task . We first propose a convex formulation for multi-task metric learning by modeling the task relationships in the form of a task covariance matrix . Then we regard transfer learning as a special case of multi-task learning and adapt the formulation of multi-task metric learning to the transfer learning setting for our method , called transfer metric learning ( TML ) . In TML , we learn the metric and the task covariances between the source tasks and the target task under a unified convex formulation . To solve the convex optimization problem , we use an alternating method in which each subproblem has an efficient solution . Experimental results on some commonly used transfer learning applications demonstrate the effectiveness of our method .
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