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ICML
2004
IEEE
15 years 10 months ago
Automated hierarchical mixtures of probabilistic principal component analyzers
Many clustering algorithms fail when dealing with high dimensional data. Principal component analysis (PCA) is a popular dimensionality reduction algorithm. However, it assumes a ...
Ting Su, Jennifer G. Dy
MMAS
2011
Springer
14 years 4 months ago
Scalable Bayesian Reduced-Order Models for Simulating High-Dimensional Multiscale Dynamical Systems
While existing mathematical descriptions can accurately account for phenomena at microscopic scales (e.g. molecular dynamics), these are often high-dimensional, stochastic and thei...
Phaedon-Stelios Koutsourelakis, Elias Bilionis
SDM
2008
SIAM
117views Data Mining» more  SDM 2008»
14 years 11 months ago
A Feature Selection Algorithm Capable of Handling Extremely Large Data Dimensionality
With the advent of high throughput technologies, feature selection has become increasingly important in a wide range of scientific disciplines. We propose a new feature selection ...
Yijun Sun, Sinisa Todorovic, Steve Goodison
AUTOMATICA
2005
112views more  AUTOMATICA 2005»
14 years 9 months ago
Robust maximum-likelihood estimation of multivariable dynamic systems
This paper examines the problem of estimating linear time-invariant state-space system models. In particular it addresses the parametrization and numerical robustness concerns tha...
Stuart Gibson, Brett Ninness
CVPR
2005
IEEE
15 years 11 months ago
Hallucinating Faces: TensorPatch Super-Resolution and Coupled Residue Compensation
In this paper, we propose a new face hallucination framework based on image patches, which integrates two novel statistical super-resolution models. Considering that image patches...
Wei Liu, Dahua Lin, Xiaoou Tang