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NIPS
2003
13 years 6 months ago
Kernel Dimensionality Reduction for Supervised Learning
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
ICIP
2008
IEEE
14 years 7 months ago
On the estimation of geodesic paths on sampled manifolds under random projections
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 ...
Mona Mahmoudi, Pierre Vandergheynst, Matteo Sorci
ECCV
2006
Springer
14 years 7 months ago
Riemannian Manifold Learning for Nonlinear Dimensionality Reduction
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...
Tony Lin, Hongbin Zha, Sang Uk Lee
MOBIHOC
2009
ACM
14 years 5 months ago
Delay and effective throughput of wireless scheduling in heavy traffic regimes: vacation model for complexity
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...
Yung Yi, Junshan Zhang, Mung Chiang
FOCS
2000
IEEE
13 years 9 months ago
Stable Distributions, Pseudorandom Generators, Embeddings and Data Stream Computation
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...
Piotr Indyk