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A New Local PCA SOM Algorithm
Huang Dong *,Zhang Yi,Pu Xiaorong
Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China
*Correspondence author
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Funding: 国家自然科学基金,教育部博士点基金（No.60471055,20040614017）
Opened online:18 December 2007
Accepted by: none
Citation: Huang Dong ,Zhang Yi,Pu Xiaorong .A New Local PCA SOM Algorithm[OL]. [18 December 2007] http://en.paper.edu.cn/en_releasepaper/content/17102
 
 
This paper proposes a Local PCA-SOM algorithm. The new competition measure is computational efficient, and implicitly incorporates the Mahalanobis distance and the reconstruction error. The matrix inversion or PCA decomposition for each data input is not needed as compared to the previous models. Moreover, the local data distribution is completely stored in the covariance matrix instead of the pre-defined numbers of the principal components. Thus, no priori information of the optimal principal subspace is required. Experiments on both the synthesis data and a pattern learning task are carried out to show the performance of the proposed method.
Keywords:Neural Networks; Unsupervised Learning; Local Principal Component Analysis; Self-Organizing Mapping
 
 
 

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