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Distance metric learning plays an important role in many applications, such as classification and clustering. In this paper, we propose a novel distance metric learning using two hinge losses in the objective function. One is the constraint of the pairs which makes the similar pairs (the same label) closer and the dissimilar (different labels) pairs separated as far as possible. The other one is the...
Given several related tasks, multi-task learning can improve the performance of each task through sharing parameters or feature representations. In this paper, we apply multi-task learning to a particular case of distance metric learning, in which we have a small amount of labeled data. Consider the effectiveness of semi-supervised learning handling few labeled machine learning problems, we integrate...
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