New Theory and Algorithms for Scalable Data Fusion

Abstract

The research performed under this grant served to address the modeling, algorithmic and theoretical challenges associated with problems of large-scale data fusion. Significant research accomplishments included: (a) the development of message passing algorithms for distributed optimization and inference; (b) the formulation and analysis of convex relaxations for estimating low-rank matrices from data; (c) the development of non-parametric methods for solving high-dimensional prediction problems; and (d) the analysis and implementation of methods for graphical model selection.

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Document Details

Document Type
Technical Report
Publication Date
Jul 14, 2013
Accession Number
ADA588861

Entities

People

  • Martin J. Wainwright

Organizations

  • University of California, Berkeley

Tags

Communities of Interest

  • Autonomy
  • Energy and Power Technologies
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Air Force
  • Air Force Research Laboratories
  • Algorithms
  • Covariance
  • Data Analysis
  • Data Fusion
  • Detectors
  • Estimators
  • Information Science
  • Information Theory
  • Networks
  • Political Science
  • Probability
  • Sensor Networks
  • Signal Processing
  • Simulations
  • Statistical Inference

Readers

  • Computer Networking
  • Linear Algebra
  • Regression Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms