Support Vector Machines, SVMs, and the Large Margin Nearest Neighbor algorithm, LMNN, are two very popular learning algorithms with quite different learning biases. In this paper we bring them into a unified view and show that they have a much stronger relation than what is commonly thought. We analyze SVMs from a metric learning perspective and cast them as a metric learning problem, a view wh...

Learning a good distance function is crucial in a lot of applications. Different Learning distance with different will effect the performance of application in different ways. The most closely related application is classification and retrieval task. In this report, three different distance learning algorithm, LMNN, Isomap and SimpleNPKL, is compared via their performance in three different kin...

Nearest neighbor (NN) classifier with dynamic time warping (DTW) is considered to be an effective method for time series classification. The performance of NN-DTW is dependent on the DTW constraints because the NN classifier is sensitive to the used distance function. For time series classification, the global path constraint of DTW is learned for optimization of the alignment of time series by...