We propose a novel method of dimensionality reduction for supervised learning. Given a regression or classification problem in which we wish to predict a variable Y from an expla...
Kenji Fukumizu, Francis R. Bach, Michael I. Jordan
In this paper, we focus on the use of random projections as a dimensionality reduction tool for sampled manifolds in highdimensional Euclidean spaces. We show that geodesic paths ...
In recent years, nonlinear dimensionality reduction (NLDR) techniques have attracted much attention in visual perception and many other areas of science. We propose an efficient al...
Distributed scheduling algorithms for wireless ad hoc networks have received substantial attention over the last decade. The complexity levels of these algorithms span a wide spec...
In this article, we show several results obtained by combining the use of stable distributions with pseudorandom generators for bounded space. In particular: —We show that, for a...