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《Computer Engineering and Applications》 2007-28
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Data dimensionality reduction algorithm when source data is spare

SONG Xin1,3,YE Shi-wei2 1.College of Engineering of the Graduate School of the Chinese Academy of Sciences,Beijing 100049,China 2.School of Information Science and Engineering of the Graduate School of the Chinese Academy of Sciences,Beijing 100049,China 3.Northeastern University at Qinhuangdao,Qinhuangdao,Heibei 066004,China  
Manifold learning is a nonlinear data dimensionality reduction method.It is proposed according to the manifold concept.The main idea of manifold leaning is to find a smooth low-dimensional manifold embedded in the high-dimensional data space.The Locally Linear Embedding(LLE) algorithm based on Manifold learning is introduced firstly in this paper,because the conventional LLE algorithm will be ineffective when the source data is spare.,with that the manifold learning algorithm based on Locally Linear Approximating(LLA) is presented.At last,the results show the effectiveness of the LLA on the S-curse sampling and testing.
【Fund】: 国家自然科学基金(the National Natural Science Foundation of China under Grant No.60435010);; 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(the"985 project"Informationization Cadre Key Technique Science and Technology Innovation Platform of Northeastern University)
【CateGory Index】: TP181
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