@inproceedings{16a0fcb267aa4e00beffa3ca1c60f0f1,
title = "Stratification Learning: Detecting Mixed Density and Dimensionality in High Dimensional Point Clouds",
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.",
author = "Gloria Haro and Gregory Randall and Guillermo Sapiro",
note = "Publisher Copyright: {\textcopyright} NIPS 2006.All rights reserved; 19th International Conference on Neural Information Processing Systems, NIPS 2006 ; Conference date: 04-12-2006 Through 07-12-2006",
year = "2006",
language = "English (US)",
series = "NIPS 2006: Proceedings of the 19th International Conference on Neural Information Processing Systems",
publisher = "MIT Press Journals",
pages = "553--560",
editor = "Bernhard Scholkopf and Platt, {John C.} and Thomas Hofmann",
booktitle = "NIPS 2006",
address = "United States",
}