Discriminant Analysis with Label Constrained Graph Partition

Authors:
Peng Guan;Yaoliang Yu;Liming Zhang
Affiliations:
Department of Electronic Engineering, Fudan University, Shanghai 200433, China;Department of Electronic Engineering, Fudan University, Shanghai 200433, China;Department of Electronic Engineering, Fudan University, Shanghai 200433, China
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
Year:
2007

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Abstract

In this paper, a space partition method called "Label Constrained Graph Partition" (LCGP) is presented to solve the Sample-Interweaving-Phenomenon in the original space. We first divide the entire training set into subclasses by means of LCGP, so that the scopes of subclasses will not overlap in the original space. Then "Most Discriminant Subclass Distribution" (MDSD) criterion is proposed to decide the best partition result. At last, typical LDA algorithm is applied to obtain the feature space and the RBF neural network classifier is utilized to make the final decision. The computer simulations and comparisons are given to demonstrate the performance of our method.