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On the estimation of geodesic paths on sampled manifolds under random projections

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On the estimation of geodesic paths on sampled manifolds under random projections
In this paper, we focus on the use of random projections as a dimensionality reduction tool for sampled manifolds in highdimensional Euclidean spaces. We show that geodesic paths approximations from nearest neighbors Euclidean distances are well-preserved by Gaussian projections and we characterize the distribution of geodesic lengths in the reduced dimensional point cloud. A stylized application to a real-world data set of human faces is presented to validate our theoretical findings.
Mona Mahmoudi, Pierre Vandergheynst, Matteo Sorci
Added 20 Oct 2009
Updated 19 Jul 2010
Type Conference
Year 2008
Where ICIP
Authors Mona Mahmoudi, Pierre Vandergheynst, Matteo Sorci
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