Cost-based Reweighting for Principal Lq Support Vector Machines for Sufficient Dimension Reduction
DOI : 10.3844/jmssp.2019.218.224
Journal of Mathematics and Statistics
Volume 15, 2019
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.
© 2019 Andreas Artemiou. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.