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ICASSP
2011
IEEE
13 years 3 months ago
Supervised nonlinear spectral unmixing using a polynomial post nonlinear model for hyperspectral imagery
This paper studies a hierarchical Bayesian model for nonlinear hyperspectral image unmixing. The proposed model assumes that the pixel reflectances are polynomial functions of li...
Yoann Altmann, Abderrahim Halimi, Nicolas Dobigeon...
IJDMMM
2010
128views more  IJDMMM 2010»
13 years 10 months ago
Graphical models based hierarchical probabilistic community discovery in large-scale social networks
: Real-world social networks, while disparate in nature, often comprise of a set of loose clusters (a.k.a. communities), in which members are better connected to each other than to...
Haizheng Zhang, Ke Ke, Wei Li, Xuerui Wang
ICASSP
2010
IEEE
13 years 10 months ago
A hierarchical Bayesian model for frame representation
In many signal processing problems, it may be fruitful to represent the signal under study in a redundant linear decomposition called a frame. If a probabilistic approach is adopt...
Lotfi Chaâri, Jean-Christophe Pesquet, Jean-...
TSP
2008
105views more  TSP 2008»
13 years 11 months ago
Semi-Supervised Linear Spectral Unmixing Using a Hierarchical Bayesian Model for Hyperspectral Imagery
This paper proposes a hierarchical Bayesian model that can be used for semi-supervised hyperspectral image unmixing. The model assumes that the pixel reflectances result from linea...
Nicolas Dobigeon, Jean-Yves Tourneret, Chein-I Cha...
CSDA
2007
105views more  CSDA 2007»
13 years 11 months ago
Joint segmentation of wind speed and direction using a hierarchical model
The problem of detecting changes in wind speed and direction is considered. Bayesian priors, with various degrees of certainty, are used to represent relationships between the two...
Nicolas Dobigeon, Jean-Yves Tourneret
CORR
2008
Springer
129views Education» more  CORR 2008»
13 years 11 months ago
Hierarchical Bayesian sparse image reconstruction with application to MRFM
This paper presents a hierarchical Bayesian model to reconstruct sparse images when the observations are obtained from linear transformations and corrupted by an additive white Gau...
Nicolas Dobigeon, Alfred O. Hero, Jean-Yves Tourne...
WSC
1998
14 years 26 days ago
Bayesian Model Selection when the Number of Components is Unknown
In simulation modeling and analysis, there are two situations where there is uncertainty about the number of parameters needed to specify a model. The first is in input modeling w...
Russell C. H. Cheng
EMNLP
2008
14 years 29 days ago
Unsupervised Multilingual Learning for POS Tagging
We demonstrate the effectiveness of multilingual learning for unsupervised part-of-speech tagging. The key hypothesis of multilingual learning is that by combining cues from multi...
Benjamin Snyder, Tahira Naseem, Jacob Eisenstein, ...
ICDM
2007
IEEE
153views Data Mining» more  ICDM 2007»
14 years 5 months ago
HSN-PAM: Finding Hierarchical Probabilistic Groups from Large-Scale Networks
Real-world social networks are often hierarchical, reflecting the fact that some communities are composed of a few smaller, sub-communities. This paper describes a hierarchical B...
Haizheng Zhang, Wei Li, Xuerui Wang, C. Lee Giles,...
ICML
2005
IEEE
15 years 9 days ago
Dirichlet enhanced relational learning
We apply nonparametric hierarchical Bayesian modelling to relational learning. In a hierarchical Bayesian approach, model parameters can be "personalized", i.e., owned b...
Zhao Xu, Volker Tresp, Kai Yu, Shipeng Yu, Hans-Pe...