Distance functions are an important component in many learning applications. However, the correct function is context dependent, therefore it is advantageous to learn a distance f...
We study the problem of projecting high-dimensional tensor data on an unspecified Riemannian manifold onto some lower dimensional subspace1 without much distorting the pairwise geo...
In this paper we propose a new distance function (rank distance) designed to reflect stylistic similarity between texts. To assess the ability of this distance measure to capture ...
In this work we introduce a new distance estimation technique by boosting and we apply it to the K-Nearest Neighbor Classifier (KNN). Instead of applying AdaBoost to a typical cla...
An important consideration in similarity-based retrieval of moving object trajectories is the definition of a distance function. The existing distance functions are usually sensi...