70 search results - page 3 / 14 » Bypassing the embedding: algorithms for low dimensional metr... |
ICML
2005
IEEE
15 years 10 months ago
2005
IEEE
Many unsupervised algorithms for nonlinear dimensionality reduction, such as locally linear embedding (LLE) and Laplacian eigenmaps, are derived from the spectral decompositions o...
100
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NIPS
2001
14 years 11 months ago
2001
Drawing on the correspondence between the graph Laplacian, the Laplace-Beltrami operator on a manifold, and the connections to the heat equation, we propose a geometrically motiva...
113
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Publication
We consider the computational problem of finding nearest neighbors in general metric spaces. Of particular interest are spaces that may not be conveniently embedded or approximate...
111
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KDD
2008
ACM
15 years 10 months ago
2008
ACM
The success of popular algorithms such as k-means clustering or nearest neighbor searches depend on the assumption that the underlying distance functions reflect domain-specific n...
STOC
2005
ACM
15 years 10 months ago
2005
ACM
A low-distortion embedding between two metric spaces is a mapping which preserves the distances between each pair of points, up to a small factor called distortion. Low-distortion...