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» A Hilbert Space Embedding for Distributions
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AAAI
2007
13 years 7 months ago
A Kernel Approach to Comparing Distributions
We describe a technique for comparing distributions without the need for density estimation as an intermediate step. Our approach relies on mapping the distributions into a Reprod...
Arthur Gretton, Karsten M. Borgwardt, Malte J. Ras...
WACV
2012
IEEE
12 years 1 months ago
Kernel analysis over Riemannian manifolds for visual recognition of actions, pedestrians and textures
A convenient way of analysing Riemannian manifolds is to embed them in Euclidean spaces, with the embedding typically obtained by flattening the manifold via tangent spaces. This...
Mehrtash Tafazzoli Harandi, Conrad Sanderson, Arno...
IPPS
2003
IEEE
13 years 10 months ago
Partitioning with Space-Filling Curves on the Cubed-Sphere
Numerical methods for solving the systems of partial differential equations arising in geophysical fluid dynamics rely on a variety of spatial discretization schemes (e.g. finit...
John M. Dennis
COMPGEOM
2011
ACM
12 years 9 months ago
Comparing distributions and shapes using the kernel distance
Starting with a similarity function between objects, it is possible to define a distance metric (the kernel distance) on pairs of objects, and more generally on probability distr...
Sarang C. Joshi, Raj Varma Kommaraju, Jeff M. Phil...
ICASSP
2010
IEEE
13 years 5 months ago
A kernel mean matching approach for environment mismatch compensation in speech recognition
The mismatch between training and test environmental conditions presents a challenge to speech recognition systems. In this paper, we investigate an approach for matching the dist...
Abhishek Kumar, John H. L. Hansen