Research Article Open Access

Cost-based Reweighting for Principal Lq Support Vector Machines for Sufficient Dimension Reduction

Andreas Artemiou1
  • 1 Cardiff University, United Kingdom

Abstract

In this work we try to address the imbalance of the number of points which naturally occurs when slicing the response in Sufficient Dimension Reduction methods (SDR). Specifically, some recently proposed support vector machine based (SVM-based) methodology suffers a lot more due to the properties of the SVM algorithm. We target a recently proposed algorithm called Principal LqSVM and we propose the reweighting based on a different cost. We demonstrate that our reweighted proposal works better than the original algorithm in simulated and real data.

Journal of Mathematics and Statistics
Volume 15 No. 1, 2019, 218-224

DOI: https://doi.org/10.3844/jmssp.2019.218.224

Submitted On: 11 July 2019 Published On: 5 September 2019

How to Cite: Artemiou, A. (2019). Cost-based Reweighting for Principal Lq Support Vector Machines for Sufficient Dimension Reduction. Journal of Mathematics and Statistics, 15(1), 218-224. https://doi.org/10.3844/jmssp.2019.218.224

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Keywords

  • Support Vector Machines
  • Kernel Methods
  • Imbalance