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Achievable Rates for Pattern Recognition.

M Brandon Westover1, Joseph A O'Sullivan2

  • 1Department of Neurology, Massachusetts General Hospital, Boston, MA 02114-2622 USA.

IEEE Transactions on Information Theory
|March 11, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a mathematical model for pattern recognition systems, detailing the trade-off between data representation resources and environmental complexity. It establishes information-theoretic bounds for reliable pattern classification under resource constraints.

Keywords:
Distributed source codingmultiterminal information theorypattern recognition

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Area of Science:

  • Cognitive Science
  • Computer Science
  • Information Theory

Background:

  • Pattern recognition systems, both biological and artificial, face challenges in classifying unknown patterns using stored memory representations.
  • High-dimensional data and numerous patterns necessitate compressed representations, creating a trade-off between resource allocation and recognition reliability.

Purpose of the Study:

  • To develop a mathematical model characterizing the resource-complexity trade-off in pattern recognition systems.
  • To establish information-theoretic bounds for achievable recognition rates under resource constraints.

Main Methods:

  • Formulated a mathematical model for resource-constrained pattern recognition.
  • Derived single-letter information-theoretic bounds on achievable rates.
  • Analyzed specific cases with binary and Gaussian pattern data.

Main Results:

  • Quantified the trade-off between data representation resources and environmental complexity.
  • Established fundamental limits on pattern recognition performance based on available information rates.
  • Demonstrated the model's applicability to binary and Gaussian data distributions.

Conclusions:

  • The model provides a framework for understanding the limits of pattern recognition under resource constraints.
  • Information-theoretic bounds offer crucial insights into the design and capabilities of recognition systems.
  • Future work can extend this framework to more complex data types and environments.