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

Mario Sznaier (), Octavia Camps, Necmiye Ozay, Tao Ding, Gilead Tadmor and Dana Brooks
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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

Chapter Chapter 1 in Dynamics of Information Systems, 2010, pp 1-27 from Springer

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.

Keywords: Video Sequence; Data Stream; Locally Linear Embedding; Hankel Matrix; Dynamic Texture (search for similar items in EconPapers)
Date: 2010
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Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-1-4419-5689-7_1

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DOI: 10.1007/978-1-4419-5689-7_1

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