148 search results - page 20 / 30 » Riemannian Manifold Learning for Nonlinear Dimensionality Re... |
TKDE
2008
13 years 7 months ago
2008
One of the fundamental problems in Content-Based Image Retrieval (CBIR) has been the gap between low-level visual features and high-level semantic concepts. To narrow down this gap...
ICML
2007
IEEE
14 years 8 months 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...
ICANN
2003
Springer
14 years 21 days 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...
ICPR
2008
IEEE
14 years 8 months 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...
NN
2002
Springer
13 years 7 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...