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

In: Dynamics of Information Systems

Author

Listed:
  • Mario Sznaier

    (Northeastern University)

  • Octavia Camps

    (Northeastern University)

  • Necmiye Ozay

    (Northeastern University)

  • Tao Ding

    (Penn State University)

  • Gilead Tadmor

    (Northeastern University)

  • Dana Brooks

    (Northeastern University)

Abstract

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.

Suggested Citation

  • Mario Sznaier & Octavia Camps & Necmiye Ozay & Tao Ding & Gilead Tadmor & Dana Brooks, 2010. "The Role of Dynamics in Extracting Information Sparsely Encoded in High Dimensional Data Streams," Springer Optimization and Its Applications, in: Michael J. Hirsch & Panos M. Pardalos & Robert Murphey (ed.), Dynamics of Information Systems, chapter 0, pages 1-27, Springer.
    • Handle: RePEc:spr:spochp:978-1-4419-5689-7_1
    DOI: 10.1007/978-1-4419-5689-7_1
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