Loading...
Thumbnail Image
Item

Maximum gradient dimensionality reduction

Abstract
We propose a novel dimensionality reduction approach based on the gradient of the regression function. Our approach is conceptually similar to Principal Component Analysis, however instead of seeking a low dimensional representation of the predictors that preserve the sample variance, we project onto a basis that preserves those predictors which induce the greatest change in the response. Our approach has the benefits of being simple and easy to implement and interpret, while still remaining very competitive with sophisticated state-of-the-art approaches.
Type
Conference Contribution
Type of thesis
Series
Citation
Luo, X., & Durrant, R. J. (2018). Maximum gradient dimensionality reduction. In Proceedings of 2018 24th International Conference on Pattern Recognition (ICPR) (pp. 501–506). Washington, DC, USA: IEEE. https://doi.org/10.1109/ICPR.2018.8546198
Date
2018
Publisher
IEEE
Degree
Supervisors
Rights
This is an author’s accepted version of an article published in the Proceedings of 2018 24th International Conference on Pattern Recognition (ICPR). © 2018 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.