Unsupervised Learning from Multiple Information Sources Based on Non-negative Matrix Factorization (NMF)

Abstract

In many real-world applications, the data are naturally multi-modal, in the sense that they are represented by multiple sets of features. In general, with the availability of multiple information sources, it is a challenging problem to conduct integrated exploratory analysis with the aim of extracting more information than what is possible from only a single source.

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Document Details

Document Type
Technical Report
Publication Date
Jan 20, 2015
Accession Number
ADA621842

Entities

People

  • Tao Li

Organizations

  • Florida International University

Tags

Communities of Interest

  • Autonomy
  • Weapons Technologies

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Artificial Intelligence Software
  • Automata Theory
  • Automated Text Summarization
  • Classification
  • Computer Programs
  • Computer Science
  • Cross Domain
  • Data Mining
  • Disasters
  • Emergency Response
  • Information Science
  • Natural Language Processing
  • Network Science
  • Statistics
  • Students
  • Supervised Machine Learning

Fields of Study

  • Computer science

Readers

  • Computer Vision.
  • Linear Algebra
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.

Technology Areas

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