Abstract
AbstractMotivated by several recent work that adopt support vector machines into the sufficient dimension reduction research, we propose a local support vector machine based dimension reduction approach. The proposal deals with continuous and binary responses, linear and nonlinear dimension reduction in a unified framework. The localization can also help relax the stringent probabilistic assumptions required by the global methods. Numerical experiments and a real data application demonstrate the efficacy of the proposed approach.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: Statistical Analysis and Data Mining: The ASA Data Science Journal
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.