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ICA
2010
Springer
13 years 8 months ago
Binary Sparse Coding
We study a sparse coding learning algorithm that allows for a simultaneous learning of the data sparseness and the basis functions. The algorithm is derived based on a generative m...
Marc Henniges, Gervasio Puertas, Jörg Bornsch...
CSDA
2010
165views more  CSDA 2010»
13 years 7 months ago
A two-component Weibull mixture to model early and late mortality in a Bayesian framework
A two component parametric mixture is proposed to model survival after an invasive treatment, when patients may experience different hazards regimes: a risk of early mortality dir...
Alessio Farcomeni, Alessandra Nardi
MM
2003
ACM
132views Multimedia» more  MM 2003»
14 years 22 days ago
On image auto-annotation with latent space models
Image auto-annotation, i.e., the association of words to whole images, has attracted considerable attention. In particular, unsupervised, probabilistic latent variable models of t...
Florent Monay, Daniel Gatica-Perez
CDC
2009
IEEE
126views Control Systems» more  CDC 2009»
13 years 10 months ago
An approach for the state estimation of Takagi-Sugeno models and application to sensor fault diagnosis
— In this paper, a new method to design an observer for nonlinear systems described by Takagi-Sugeno (TS) model, with unmeasurable premise variables, is proposed. Most of existin...
Dalil Ichalal, Benoît Marx, José Rago...
ICML
2006
IEEE
14 years 8 months ago
Local distance preservation in the GP-LVM through back constraints
The Gaussian process latent variable model (GP-LVM) is a generative approach to nonlinear low dimensional embedding, that provides a smooth probabilistic mapping from latent to da...
Joaquin Quiñonero Candela, Neil D. Lawrence