154 search results - page 20 / 31 » Incremental Nonlinear Dimensionality Reduction by Manifold L... |
98
Voted
ICML
2007
IEEE
16 years 14 days ago
2007
IEEE
The Gaussian process latent variable model (GP-LVM) is a powerful approach for probabilistic modelling of high dimensional data through dimensional reduction. In this paper we ext...
79
Voted
ICANN
2003
Springer
15 years 4 months ago
2003
Springer
Locally linear embedding (LLE) is a recently proposed method for unsupervised nonlinear dimensionality reduction. It has a number of attractive features: it does not require an ite...
106
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ICPR
2008
IEEE
16 years 26 days ago
2008
IEEE
Embedding images into a low dimensional space has a wide range of applications: visualization, clustering, and pre-processing for supervised learning. Traditional dimension reduct...
128
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NN
2002
Springer
14 years 11 months ago
2002
Springer
The self-organising map (SOM) has been successfully employed as a nonparametric method for dimensionality reduction and data visualisation. However, for visualisation the SOM requ...
113
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MM
2006
ACM
15 years 5 months ago
2006
ACM
In many image retrieval applications, the mapping between highlevel semantic concept and low-level features is obtained through a learning process. Traditional approaches often as...