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权重马氏距离高斯核在谱分割中的应用

Application of WMD Gaussian kernel in spectral partitioning
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摘要 为了使经典谱分割的Nystrm采样快速算法得到更清晰的结果,将权重马氏距离高斯核应用于其中,相对于常用的马氏距离高斯核,得到了更好的分割效果。结果表明,使用权重马氏距离高斯核更能准确的反映两个向量的相似度,从而实现准确的分割。 To obtain a better segmentation result, this paper used Weighted Mahalanobis Distance （WMD） Gaussian kernel for Nystrom-Ncut segmentation. It proves that weighted Mahalanobis distance Gaussian kernel is more appropriate for spectralgraph theoretic methods than Mahalanobis distance, because weighted Mahalanobis distance can compute the similarity between two pixels more accurately.
作者 陈应良 王士同
机构地区 江南大学信息工程学院
出处 《计算机应用》 CSCD 北大核心 2008年第7期1738-1741,共4页 journal of Computer Applications
关键词 谱分割 聚类 Ncut Nystrfim估计 权重马氏距离 spectral graph partition clustering Ncut Nystrom approximation Weighted Mahalanobis Distance （WMD）
分类号 P391.41 [天文地球—地球物理学]
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参考文献11

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共引文献32

计算机应用
2008年 第7期

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