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Robust and Stable Locally Linear Embedding

机译:鲁棒稳定的局部线性嵌入

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摘要

Recently, some manifold learning methods have aroused a great of interest in many fields of information processing. However, these manifold learning methods are not robust against outliers. In this paper, an outlier detection algorithm is proposed, and we propose a robustand stable locally liner embedding(RSLLE) algorithm by introducing multiple linearly independent local weight vectors to represent the local geometry for each neighborhoods of clean data points. For the outlier points, RSLLE learns the local geometry by using asingle weight vector. Numerical examples are given to show the improvement and efficiency of the proposed algorithm.
机译:近来,一些多种学习方法引起了信息处理的许多领域的极大兴趣。但是,这些多方面的学习方法不能有效地抵抗异常值。本文提出了一种离群值检测算法,并通过引入多个线性独立的局部权重向量来表示每个干净数据点邻域的局部几何形状,提出了一种鲁棒且稳定的局部线性嵌入(RSLLE)算法。对于离群点,RSLLE通过使用单个权重向量来学习局部几何形状。数值算例表明了该算法的改进和有效性。

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