The k-Nearest Neighbors algorithm can be easily adapted to classify complex objects (e.g. sets, graphs) as long as a proper dissimilarity function is given over an input space. Bo...
Adam Woznica, Alexandros Kalousis, Melanie Hilario
Abstract. This paper addresses the problem of clustering images of objects seen from different viewpoints. That is, given an unlabelled set of images of n objects, we seek an unsup...
We propose a distributed algorithm for solving Euclidean metric realization problems arising from large 3D graphs, using only noisy distance information, and without any prior kno...
Given a set of n query points in a general metric space, a metricspace skyline (MSS) query asks what are the closest points to all these query points in the database. Here, consid...
We propose an unsupervised “local learning” algorithm for learning a metric in the input space. Geometrically, for a given query point, the algorithm finds the minimum volume ...