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《Computer Engineering》 2008-08
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Data Dimensionality Reduction Algorithm Based on Direct Estimate Grads

SONG Xin1,3,YE Shi-wei2(1.College of Engineering,Graduate University of Chinese Academy of Sciences,Beijing 100049;2.School of Info.Science and Engineering,Graduate University of Chinese Academy of Sciences,Beijing 100049;3.Northeastern University at Qinhuangdao,Qinhuangdao 066004)  
The dimensionality reduction techniques are very important for high-dimensional nonlinear data to complete the processing and analysis of the high complexity data source.From the view of topology,the process of dimensionality reduction is to find a low-dimensional linear or nonlinear manifold embedded in the high-dimensional data.On the basis of locally embedding manifold learning algorithms,a data dimensionality reduction algorithm based on direct estimate grads is proposed.The dimensionality reduction of the high-dimensionality nonlinear data is achieved by locally linear error approximating minimum.The simulation results of Swiss roll curse sampling and test show the significant effectiveness of the proposed algorithm.
【Fund】: 国家自然科学基金资助项目(60435010);; 河北省教育厅2006年科研计划资助项目(Z2006303);; 东北大学“985工程”信息化基础结构关键技术科技创新平台项目
【CateGory Index】: TP391.41
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