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《Computer Simulation》 2008-07
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A Manifold Learning Algorithm Based on Locally Linear Approximating

SONG Xin1,3,YE Shi-wei2(1.College of Engineering,Graduate School of the Chinese Academy of Sciences,Beijing 100049,China;2.School of Information Science and Engineering,Graduate School of the Chinese Academy of Sciences,Beijing 100049,China;3.Northeastern University in Qinhuangdao,Qinhuangdao Hebei 066004,China)  
Manifold learning is a kind of nonlinear data dimensionality reduction method based on the definition of manifold.The main concept is to find out the low-dimensional smooth manifold embedded in the high-dimensional data space.The Locally Linear Embedding(LLE) Algorithm is applied widely,and one main disadvantage of the traditional Locally Linear Embedding method is that it will turn invalid when it deals with the spare source data,but in practice dealing with the problem of source data sparsity has to be confronted with in many cases.This paper comes up with the manifold learning algorithm based on locally linear approximating by analyzing the LLE Algorithm.It reaches the aim of locally linear approximating through adopting the way of assessing the grads directly,thus realizing the dimensionality reduction of the high-dimensional nonlinear data and finally achieves the fine effect of dimensionality reduction while sampling on the S-curve to test the sparsity.
【Fund】: 国家自然科学基金资助项目(60435010)
【CateGory Index】: TP181
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