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The Role of Dynamics in Extracting Information Sparsely Encoded in High Dimensional Data Streams

  • Mario Sznaier
  • Octavia Camps
  • Necmiye Ozay
  • Tao Ding
  • Gilead Tadmor
  • Dana Brooks
Chapter
Part of the Springer Optimization and Its Applications book series (SOIA, volume 40)

Summary

A major roadblock in taking full advantage of the recent exponential growth in data collection and actuation capabilities stems from the curse of dimensionality. Simply put, existing techniques are ill-equipped to deal with the resulting overwhelming volume of data. The goal of this chapter is to show how the use of simple dynamical systems concepts can lead to tractable, computationally efficient algorithms for extracting information sparsely encoded in multimodal, extremely large data sets. In addition, as shown here, this approach leads to nonentropic information measures, better suited than the classical, entropy-based information theoretic measure, to problems where the information is by nature dynamic and changes as it propagates through a network where the nodes themselves are dynamical systems.

Keywords

Video Sequence Data Stream Locally Linear Embedding Hankel Matrix Dynamic Texture 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Mario Sznaier
    • 1
  • Octavia Camps
    • 1
  • Necmiye Ozay
    • 1
  • Tao Ding
    • 2
  • Gilead Tadmor
    • 1
  • Dana Brooks
    • 1
  1. 1.ECE DepartmentNortheastern UniversityBostonUSA
  2. 2.Department of Electrical EngineeringPenn State UniversityUniversity ParkUSA

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