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ICASSP
2010
IEEE
15 years 2 months ago
Hierarchical Gaussian Mixture Model
Gaussian mixture models (GMMs) are a convenient and essential tool for the estimation of probability density functions. Although GMMs are used in many research domains from image ...
Vincent Garcia, Frank Nielsen, Richard Nock
112
Voted
ICML
2004
IEEE
16 years 3 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
INFORMATICALT
2000
79views more  INFORMATICALT 2000»
15 years 2 months ago
Influence of Projection Pursuit on Classification Errors: Computer Simulation Results
Abstract. Influence of projection pursuit on classification errors and estimates of a posteriori probabilities from the sample is considered. Observed random variable is supposed t...
Gintautas Jakimauskas, Ricardas Krikstolaitis
152
Voted
TIP
2002
179views more  TIP 2002»
15 years 2 months ago
Unsupervised image classification, segmentation, and enhancement using ICA mixture models
An unsupervised classification algorithm is derived by modeling observed data as a mixture of several mutually exclusive classes that are each described by linear combinations of i...
Te-Won Lee, Michael S. Lewicki
MICCAI
2008
Springer
16 years 3 months ago
MR Brain Tissue Classification Using an Edge-Preserving Spatially Variant Bayesian Mixture Model
In this paper, a spatially constrained mixture model for the segmentation of MR brain images is presented. The novelty of this work is a new, edge preserving, smoothness prior whic...
Giorgos Sfikas, Christophoros Nikou, Nikolas P. ...