Sample-Starved Large Scale Network Analysis

Abstract

In this research project we developed correlation mining methods to answer the following fundamental question about complex networks: What are the fundamental limits on the amount of information that can be inferred about a network from a small number n of indirect empirical observations? In these terms, the overall objective was to develop algorithms and establish performance limits for mining information from correlation networks. The focus was on the sample starved regime arises when the number of variables (columns of the correlation matrix) is of the same order or larger than the number of observations available to estimate or detect patterns in the matrix.

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Document Details

Document Type
Technical Report
Publication Date
May 05, 2016
Accession Number
AD1009254

Entities

People

  • Alfred O. Hero
  • Bala Rajaratnam

Organizations

  • Board of Regents of the University of Michigan

Tags

Communities of Interest

  • Biomedical
  • Cyber
  • Energy and Power Technologies
  • Space

DTIC Thesaurus Topics

  • Air Force Research Laboratories
  • Algorithms
  • Automated Target Recognition
  • Climate Change
  • Computational Science
  • Data Analysis
  • Data Mining
  • Data Science
  • Image Processing
  • Information Processing
  • Information Science
  • Mathematics
  • Military Research
  • Network Science
  • Probability
  • Signal Processing
  • Statistical Algorithms

Readers

  • Distributed Systems and Data Platform Development
  • Fluid Dynamics.
  • Theoretical Analysis.

Technology Areas

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