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.
Cite
Text
Haro et al. "Stratification Learning: Detecting Mixed Density and Dimensionality in High Dimensional Point Clouds." Neural Information Processing Systems, 2006.Markdown
[Haro et al. "Stratification Learning: Detecting Mixed Density and Dimensionality in High Dimensional Point Clouds." Neural Information Processing Systems, 2006.](https://mlanthology.org/neurips/2006/haro2006neurips-stratification/)BibTeX
@inproceedings{haro2006neurips-stratification,
title = {{Stratification Learning: Detecting Mixed Density and Dimensionality in High Dimensional Point Clouds}},
author = {Haro, Gloria and Randall, Gregory and Sapiro, Guillermo},
booktitle = {Neural Information Processing Systems},
year = {2006},
pages = {553-560},
url = {https://mlanthology.org/neurips/2006/haro2006neurips-stratification/}
}