148 search results - page 20 / 30 » Riemannian Manifold Learning for Nonlinear Dimensionality Re... |
140
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TKDE
2008
15 years 1 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...
117
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ICML
2007
IEEE
16 years 2 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...
100
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ICANN
2003
Springer
15 years 7 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...
124
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ICPR
2008
IEEE
16 years 3 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...
153
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NN
2002
Springer
15 years 1 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...