88 search results - page 8 / 18 » Distance Preserving Dimension Reduction for Manifold Learnin... |
ICML
2010
IEEE
14 years 10 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...
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NN
2002
Springer
14 years 9 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...
CORR
2008
Springer
14 years 9 months ago
2008
Springer
Traditional Non-Negative Matrix Factorization (NMF) [19] is a successful algorithm for decomposing datasets into basis function that have reasonable interpretation. One problem of...
CVPR
2004
IEEE
15 years 11 months ago
2004
IEEE
Can we detect low dimensional structure in high dimensional data sets of images? In this paper, we propose an algorithm for unsupervised learning of image manifolds by semidefinit...
NPL
2006
14 years 9 months ago
2006
This paper presents a novel dimension reduction algorithm for kernel based classification. In the feature space, the proposed algorithm maximizes the ratio of the squared between-c...