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A regularization path algorithm for support vector ordinal regression.

Bin Gu1

  • 1Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science & Technology, Nanjing, PR China; School of Computer & Software, Nanjing University of Information Science & Technology, Nanjing, PR China.

Neural Networks : the Official Journal of the International Neural Network Society
|December 12, 2017
PubMed
Summary
This summary is machine-generated.

We introduce a novel regularization path algorithm for Support Vector Ordinal Regression (SVOR). This method efficiently tracks SVOR variables, enhancing model selection for ordinal regression tasks.

Keywords:
QR decompositionSingularitySolution pathSupport vector ordinal regression

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Area of Science:

  • Machine Learning
  • Statistical Modeling

Background:

  • Support Vector Ordinal Regression (SVOR) is a key method for ordinal regression.
  • Solution path algorithms offer valuable insights for model selection in machine learning.
  • Existing SVOR formulations lack efficient solution path algorithms due to complexity.

Purpose of the Study:

  • To develop a regularization path algorithm for SVOR.
  • To enable efficient tracking of SVOR variables across regularization parameters.
  • To improve model selection capabilities for SVOR.

Main Methods:

  • A novel regularization path algorithm is proposed for SVOR.
  • QR decomposition is utilized to manage singular matrices within the path computation.
  • The algorithm tracks two sets of SVOR variables with respect to the regularization parameter.

Main Results:

  • The proposed regularization path algorithm for SVOR is effective across diverse datasets.
  • The algorithm successfully tracks SVOR variables as the regularization parameter varies.
  • The regularization path approach demonstrates superiority in SVOR model selection.

Conclusions:

  • The developed regularization path algorithm addresses a significant gap in SVOR methodology.
  • This advancement facilitates more robust and efficient model selection for ordinal regression problems.
  • The algorithm's effectiveness and superiority are validated through empirical experiments.