Supervised independent component analysis by maximizing relative entropy

摘要

This paper presents a novel algorithm called SICA-MRE (supervised independent component analysis by maximizing relative entropy). It removes the disadvantage in traditional ICA, which ignores the contributions of independent components to recognition performance. Experimental results in face and iris recognition show that the presented algorithm has better performance.