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IPPS
1998
IEEE
13 years 10 months ago
Capturing the Connectivity of High-Dimensional Geometric Spaces by Parallelizable Random Sampling Techniques
Abstract. Finding paths in high-dimensional gemetric spaces is a provably hard problem. Recently, a general randomized planning scheme has emerged as an e ective approach to solve ...
David Hsu, Lydia E. Kavraki, Jean-Claude Latombe, ...
NIPS
2001
13 years 7 months ago
Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering
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...
Mikhail Belkin, Partha Niyogi
CVPR
2004
IEEE
14 years 7 months ago
Unsupervised Learning of Image Manifolds by Semidefinite Programming
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...
Kilian Q. Weinberger, Lawrence K. Saul
PAMI
2006
117views more  PAMI 2006»
13 years 5 months ago
Metric Learning for Text Documents
High dimensional structured data such as text and images is often poorly understood and misrepresented in statistical modeling. The standard histogram representation suffers from ...
Guy Lebanon