Stratification Learning: Detecting Mixed Density and Dimensionality in High Dimensional Point Clouds (PREPRINT)

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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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 2006
Accession Number
ADA478614

Entities

People

  • Gloria Haro
  • Gregory Randall
  • Guillermo Sapiro

Organizations

  • University of Minnesota

Tags

Communities of Interest

  • Ground and Sea Platforms
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Classification
  • Clustering
  • Computations
  • Dimensionality Reduction
  • Estimators
  • Gaussian Noise
  • Geometry
  • Image Segmentation
  • Learning
  • Mathematical Analysis
  • Mathematics
  • Noise
  • Point Clouds
  • Probability
  • Probability Density Functions
  • Stratification

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