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A class-specific mean vector-based weighted competitive and collaborative representation method for classification.

Jianping Gou1, Xin He1, Junyu Lu1

  • 1School of Computer Science and Communication Engineering and Jiangsu Key Laboratory of Security Tech. for Industrial Cyberspace, Jiangsu University, Zhenjiang, 212013, China.

Neural Networks : the Official Journal of the International Neural Network Society
|March 18, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a new method, class-specific mean vector-based weighted competitive and collaborative representation (CMWCCR), to improve pattern classification. CMWCCR enhances competitive and discriminant representations for more effective classification performance.

Keywords:
Collaborative representationCollaborative representation-based classificationPattern classificationRepresentation-based classification

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Area of Science:

  • Computer Science
  • Pattern Recognition
  • Machine Learning

Background:

  • Collaborative Representation-based Classification (CRC) is effective but lacks competitive learning for discrimination.
  • Existing CRC methods do not fully leverage class-specific information for enhanced pattern discrimination.

Purpose of the Study:

  • To propose a novel CRC method, CMWCCR, for enhanced competitive and discriminant representations.
  • To improve pattern classification by integrating competitive, mean vector, and weighted constraints.

Main Methods:

  • Developed the class-specific mean vector-based weighted competitive and collaborative representation (CMWCCR) model.
  • Incorporated competitive constraints for learning representations among all classes.
  • Introduced mean vector and weighted constraints to enhance representation discrimination.

Main Results:

  • Extensive experiments were conducted on twenty-eight datasets.
  • CMWCCR was compared against state-of-the-art representation-based classification methods.
  • The proposed CMWCCR demonstrated satisfactory and robust classification performance.

Conclusions:

  • The CMWCCR model effectively enhances competitive and discriminant representations.
  • The integrated constraints in CMWCCR complement each other for superior classification.
  • CMWCCR is a robust and effective method for pattern classification tasks.