785 search results - page 69 / 157 » Semi-Supervised Dimensionality Reduction |
138
Voted
ICML
2007
IEEE
16 years 4 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...
140
Voted
KDD
2001
ACM
16 years 4 months ago
2001
ACM
The problem of similarity search (query-by-content) has attracted much research interest. It is a difficult problem because of the inherently high dimensionality of the data. The ...
162
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ECCV
2004
Springer
16 years 5 months ago
2004
Springer
Abstract. Dimensionality reduction is an essential aspect of visual processing. Traditionally, linear dimensionality reduction techniques such as principle components analysis have...
134
Voted
ICML
2010
IEEE
15 years 4 months ago
2010
IEEE
Dimension reduction is popular for learning predictive models in high-dimensional spaces. It can highlight the relevant part of the feature space and avoid the curse of dimensiona...
117
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ICML
2008
IEEE
16 years 4 months ago
2008
IEEE
In this paper we introduce a novel approach to manifold alignment, based on Procrustes analysis. Our approach differs from "semisupervised alignment" in that it results ...