An improved study of locality sensitive discriminant analysis for object recognition
Liu Liu, Fuqiang Zhou, Yuzhu He · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2015
Locality sensitive discriminant analysis (LSDA) is a method considering both the discriminant and geometrical structure of the data. Within-class graph and between-class graph are first constructed to discover both geometrical and discriminant structure of the data manifold. Then a proportional constant is used to measure the different importance of two graphs. Finally, a reasonable criterion is used to choose a good map so that the connected points of within-class graph stay as close as possible while connected points of between-class graph stay as distant as possible. The key technique of LSDA is nearest neighbor graph construction. In this paper, we compared two different nearest neighbor graph construction methods. The experiment results demonstrate that splitting a nearest neighbor into equally sized with class graph and between-class graph has smaller amount of computations while construct within-class graph and between-class graph by using different sized nearest neighbors could improving the accuracy.