2006
NIPS
NeurIPS 2006
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.
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Conference Pioneer
— NIPS 2006
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Keyword Pioneer
— stratification learning
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Machine Learning, Mathematics & Optimization, Natural Language Processing, Speech & Audio
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Interdisciplinary Bridge
— Data Science & Analytics and Machine Learning and Mathematics & Optimization
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Trend Setter
— Data Mining
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Hot Topic Early Bird
— dimensionality reduction
Authors
Topics
Machine Learning > Core Methods > Clustering
Machine Learning > Core Methods > Representation Learning
Data Science & Analytics > Methods > Data Mining
Mathematics & Optimization > Mathematics > Geometry
Machine Learning > Bayesian & Probabilistic > Probabilistic Modeling
Machine Learning > Core Methods > Dimensionality Reduction