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ESANN
2007
13 years 10 months ago
Mixtures of robust probabilistic principal component analyzers
Mixtures of probabilistic principal component analyzers model high-dimensional nonlinear data by combining local linear models. Each mixture component is specifically designed to...
Cédric Archambeau, Nicolas Delannay, Michel...
IEICET
2010
132views more  IEICET 2010»
13 years 7 months ago
Direct Importance Estimation with a Mixture of Probabilistic Principal Component Analyzers
Estimating the ratio of two probability density functions (a.k.a. the importance) has recently gathered a great deal of attention since importance estimators can be used for solvi...
Makoto Yamada, Masashi Sugiyama, Gordon Wichern, J...
ICML
2004
IEEE
14 years 10 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
ICASSP
2010
IEEE
13 years 9 months ago
Direct importance estimation with probabilistic principal component analyzers
The importance estimation problem (estimating the ratio of two probability density functions) has recently gathered a great deal of attention for use in various applications, e.g....
Makoto Yamada, Masashi Sugiyama, Gordon Wichern
ICIP
2004
IEEE
14 years 11 months ago
Estimation of mixtures of probabilistic pca with stochastic em for the 3d biplanar reconstruction of scoliotic rib cage
In this paper, we present a robust method for estimating the model parameters in a mixture of probabilistic principal component analyzers. This method is based on the Stochastic v...
François Destrempes, Jacques A. de Guise, M...