KERNEL NEIGHBORHOOD PRESERVING EMBEDDING FOR CLASSIFICATION  

作　　者：Tao Xiaoyan Ji Hongbing Men Jian 

机构地区：[1]School of Electronic Engineering, Xidian University, Xi＇an 710071, China [2]The Telecommunication Engineering Institute, Air Force Engineering University, Xi＇an 710077, China

出　　处：《Journal of Electronics(China)》2009年第3期374-379,共6页电子科学学刊（英文版）

摘　　要：The Neighborhood Preserving Embedding(NPE) algorithm is recently proposed as a new dimensionality reduction method.However, it is confined to linear transforms in the data space.For this, based on the NPE algorithm, a new nonlinear dimensionality reduction method is proposed, which can preserve the local structures of the data in the feature space.First, combined with the Mercer kernel, the solution to the weight matrix in the feature space is gotten and then the corresponding eigenvalue problem of the Kernel NPE(KNPE) method is deduced.Finally, the KNPE algorithm is resolved through a transformed optimization problem and QR decomposition.The experimental results on three real-world data sets show that the new method is better than NPE, Kernel PCA(KPCA) and Kernel LDA(KLDA) in performance.The Neighborhood Preserving Embedding （NPE） algorithm is recently proposed as a new dimensionality reduction method. However, it is confined to linear transforms in the data space. For this, based on the NPE algorithm, a new nonlinear dimensionality reduction method is proposed, which can preserve the local structures of the data in the feature space. First, combined with the Mercer kernel, the solution to the weight matrix in the feature space is gotten and then the corresponding eigenvalue problem of the Kernel NPE （KNPE） method is deduced. Finally, the KNPE algorithm is resolved through a transformed optimization problem and QR decomposition. The experimental results on three real-world data sets show that the new method is better than NPE, Kernel PCA （KPCA） and Kernel LDA （KLDA） in performance.

关 键 词：Kernel Neighborhood Preserving Embedding （KNPE） Neighborhood structure FEATUREEXTRACTION QR decomposition 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] O157.5[自动化与计算机技术—计算机科学与技术]