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» Approximation Via Value Unification
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CORR
2010
Springer
189views Education» more  CORR 2010»
13 years 6 months ago
Robust PCA via Outlier Pursuit
Singular Value Decomposition (and Principal Component Analysis) is one of the most widely used techniques for dimensionality reduction: successful and efficiently computable, it ...
Huan Xu, Constantine Caramanis, Sujay Sanghavi
HVEI
2009
13 years 5 months ago
Improved colour to greyscale via integrability correction
The classical approach to converting colour to greyscale is to code the luminance signal as a grey value image. However, the problem with this approach is that the detail at equil...
Mark S. Drew, David Connah, Graham D. Finlayson, M...
ESA
2006
Springer
139views Algorithms» more  ESA 2006»
13 years 11 months ago
Stochastic Shortest Paths Via Quasi-convex Maximization
Abstract. We consider the problem of finding shortest paths in a graph with independent randomly distributed edge lengths. Our goal is to maximize the probability that the path len...
Evdokia Nikolova, Jonathan A. Kelner, Matthew Bran...
IJCAI
2001
13 years 8 months ago
Solving Factored MDPs via Non-Homogeneous Partitioning
This paper describes an algorithm for solving large state-space MDPs (represented as factored MDPs) using search by successive refinement in the space of non-homogeneous partition...
Kee-Eung Kim, Thomas Dean
JMLR
2010
108views more  JMLR 2010»
13 years 2 months ago
Sufficient Dimension Reduction via Squared-loss Mutual Information Estimation
The goal of sufficient dimension reduction in supervised learning is to find the lowdimensional subspace of input features that is `sufficient' for predicting output values. ...
Taiji Suzuki, Masashi Sugiyama