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» Sparse Representation for Gaussian Process Models
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ICA
2007
Springer
13 years 10 months ago
Infinite Sparse Factor Analysis and Infinite Independent Components Analysis
Abstract. A nonparametric Bayesian extension of Independent Components Analysis (ICA) is proposed where observed data Y is modelled as a linear superposition, G, of a potentially i...
David Knowles, Zoubin Ghahramani
ICML
2008
IEEE
14 years 7 months ago
Sparse multiscale gaussian process regression
Most existing sparse Gaussian process (g.p.) models seek computational advantages by basing their computations on a set of m basis functions that are the covariance function of th...
Bernhard Schölkopf, Christian Walder, Kwang I...
JMLR
2010
202views more  JMLR 2010»
13 years 1 months ago
Learning the Structure of Deep Sparse Graphical Models
Deep belief networks are a powerful way to model complex probability distributions. However, it is difficult to learn the structure of a belief network, particularly one with hidd...
Ryan Prescott Adams, Hanna M. Wallach, Zoubin Ghah...
ICASSP
2011
IEEE
12 years 10 months ago
An unsupervised algorithm for hybrid/morphological signal decomposition
The main contribution presented here is an adaptive/unsupervised iterative thresholding algorithm for sparse representation of signals which can be modeled as the sum of two compo...
Matthieu Kowalski, Thomas Rodet
ICA
2010
Springer
13 years 6 months ago
SMALLbox - An Evaluation Framework for Sparse Representations and Dictionary Learning Algorithms
SMALLbox is a new foundational framework for processing signals, using adaptive sparse structured representations. The main aim of SMALLbox is to become a test ground for explorati...
Ivan Damnjanovic, Matthew E. P. Davies, Mark D. Pl...