Fisher subspace tree classifier based on neural networks

Authors:
Dongyue Chen;Xiaodan Lu;Liming Zhang
Affiliations:
Dept. Electronic Engineering, Fudan University, Shanghai, China;Dept. Electronic Engineering, Fudan University, Shanghai, China;Dept. Electronic Engineering, Fudan University, Shanghai, China
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
Year:
2005

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Abstract

This paper proposes a multi-neural network classification based on fisher transformation. The new method improves HDR[1] (Hierarchical discriminate regression) method proposed in 2000, which can classify the training set from coarse to fine by non-linear dynamic clustering for high-dimension data. In proposed method a fisher subspace replaces K-L subspace of HDR that simplifies the Hierarchical tree. Simulation results show that our method is better than HDR on recognition ratio and time cost.