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ICPR
2000
IEEE
13 years 8 months ago
Constrained Mixture Modeling of Intrinsically Low-Dimensional Distributions
In this paper we introduce a novel way of modeling distributions with a low latent dimensionality. Our method allows for a strict control of the properties of the mapping between ...
Joris Portegies Zwart, Ben J. A. Kröse
TMI
2008
136views more  TMI 2008»
13 years 4 months ago
Classification of fMRI Time Series in a Low-Dimensional Subspace With a Spatial Prior
We propose a new method for detecting activation in functional magnetic resonance imaging (fMRI) data. We project the fMRI time series on a low-dimensional subspace spanned by wave...
François G. Meyer, Xilin Shen
ICPR
2004
IEEE
14 years 5 months ago
Iterative Figure-Ground Discrimination
Figure-ground discrimination is an important problem in computer vision. Previous work usually assumes that the color distribution of the figure can be described by a low dimensio...
Liang Zhao, Larry S. Davis
NIPS
2001
13 years 5 months ago
Global Coordination of Local Linear Models
High dimensional data that lies on or near a low dimensional manifold can be described by a collection of local linear models. Such a description, however, does not provide a glob...
Sam T. Roweis, Lawrence K. Saul, Geoffrey E. Hinto...
NN
2006
Springer
13 years 4 months ago
Missing data imputation through GTM as a mixture of t-distributions
The Generative Topographic Mapping (GTM) was originally conceived as a probabilistic alternative to the well-known, neural networkinspired, Self-Organizing Maps. The GTM can also ...
Alfredo Vellido