Rough sets for enhancements of local subspace classifier
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.
rough sets local subspace classifier local principal component analysis