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ICML
2004
IEEE
16 years 5 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 11 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
153
Voted
SDM
2008
SIAM
117views Data Mining» more  SDM 2008»
15 years 5 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
125
Voted
AUTOMATICA
2005
112views more  AUTOMATICA 2005»
15 years 4 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
16 years 6 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