Subspace Methods for Massive and Messy Data

Abstract

This proposal focused on subspace estimation in various modern big-data contexts, where data are massive, streaming, time-varying, and have missing, corrupted, and ill-conditioned data. In the final stages of the project, we also begun an exploration of nonlinear generalizations of subspaces, to provide models that can capture more interesting signal variation.

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Document Details

Document Type
Technical Report
Publication Date
Jul 12, 2017
Accession Number
AD1051284

Entities

People

  • Laura Balzano

Organizations

  • University of Michigan

Tags

Communities of Interest

  • Autonomy
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Computer Vision
  • Computers
  • Data Science
  • Detectors
  • Information Processing
  • Information Science
  • Machine Learning
  • Measurement
  • Military Research
  • Multivariate Analysis
  • Neural Networks
  • Nonlinear Dynamics
  • Probability
  • Signal Processing
  • Students

Fields of Study

  • Engineering

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Distributed Systems and Data Platform Development