We show how to build hierarchical, reduced-rank representation for large stochastic matrices and use this representation to design an efficient algorithm for computing the largest...
High dimensional data that lies on or near a low dimensional manifold can be described by a collection of local linear models. Such a description, however, does not provide a glob...
Sam T. Roweis, Lawrence K. Saul, Geoffrey E. Hinto...
We present a family of methods for speeding up distributed locks by exploiting the uneven distribution of both temporal and spatial locality of access behaviour of many applicatio...
Classical learning assumes the learner is given a labeled data sample, from which it learns a model. The field of Active Learning deals with the situation where the learner begins...
This paper describes an algorithm for approximating missing data in air traffic routes thereby allowing the lengths of different routes to be compared for our simulation analyses....