Nonlinear embedding preserving multiple local-linearities

Nonlinear embedding preserving multiple local-linearities
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非线性嵌入保留多个局部线性

DOI:
10.1016/j.patcog.2009.09.014
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发表时间:
2010-04
影响因子:
8
通讯作者:
张振跃
张振跃
中科院分区:
计算机科学1区
文献类型:
--
作者:
王靖;张振跃

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局部线性嵌入(LLE)是一种有效的非线性降维算法。本文讨论了LLE的稳定性,重点是最佳的权重提取本地线性背后所考虑的流形。证明了存在多组近似最优的权值,可以用来提高LLE的稳定性。然后提出了一种新的算法,使用多个权重,以及用于构建多个权重的技术。这种算法被称为保持多重局部线性的非线性嵌入算法(NEML)。NEML改善了局部线性的保持,并且比LLE更稳定。对于等距流形,我们也给出了NEML的一个简短的分析。由于NEML和局部切空间对齐(LTSA)都采用了多个局部约束,因此在方法上进行了比较。数值算例表明了该方法的有效性。
Locally linear embedding (LLE) is one of the effective and efficient algorithms for nonlinear dimensionality reduction. This paper discusses the stability of LLE, focusing on the optimal weights for extracting local linearity behind the considered manifold. It is proven that there are multiple sets of weights that are approximately optimal and can be used to improve the stability of LLE. A new algorithm using multiple weights is then proposed, together with techniques for constructing multiple weights. This algorithm is called as nonlinear embedding preserving multiple local-linearities (NEML). NEML improves the preservation of local linearity and is more stable than LLE. A short analysis for NEML is also given for isometric manifolds. NEML is compared with the local tangent space alignment (LTSA) in methodology since both of them adopt multiple local constraints. Numerical examples are given to show the improvement and efficiency of NEML.
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发表时间: 2002-07
期刊: Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
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