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Stratification learning: Detecting mixed density and dimensionality in high dimensional point clouds (2006)
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| Venue: | In Advances in NIPS 19 |
| Citations: | 20 - 2 self |
BibTeX
@INPROCEEDINGS{Randall06stratificationlearning:,
author = {Gregory Randall and Gloria Haro and Gloria Haro and Gregory R and Guillermo Sapiro and Guillermo Sapiro},
title = {Stratification learning: Detecting mixed density and dimensionality in high dimensional point clouds},
booktitle = {In Advances in NIPS 19},
year = {2006}
}
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Abstract
The study of point cloud data sampled from a stratification, a collection of manifolds with possible different dimensions, is pursued in this paper. We present a technique for simultaneously soft clustering and estimating the mixed dimensionality and density of such structures. The framework is based on a maximum likelihood estimation of a Poisson mixture model. The presentation of the approach is completed with artificial and real examples demonstrating the importance of extending manifold learning to stratification learning. 1
