Abstract
Developed recently local subspace classifier (LSC) based on local principal component analysis (LPCA) though has high performance (99.4% on NIST database of handwritten digits) lacks a mechanism of recovery from errors. Having confidence levels associated with the decision function in the classifier could help in creation of such procedure. In this paper it is shown how rough set idea can be used to incorporate confidence mechanism. In this way we make the algorithm more adequate to practical use as human actions are now possible at low confidence decisions. Besides main goal of the paper the necessary algorithmic tools for LPCA and their theoretical base are presented, too.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: 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.
