1) weighted kernel k-means
加权核K均值
1.
An improved minimum cut model is presented considering that the minimum cut criteria favors cutting small sets of isolated nodes,then equivalence relation between the improved minimum cut model and weighted kernel k-means is researched,and the influence of different similarity functions on the results of segmentation are also analysed.
提出改进的最小割(IMC)模型以避免分割出小的孤立点集,研究了改进的最小割模型与加权核K均值之间的等价关系,列举了几种常见的用于建立图割模型边权值的相似度函数,并分析了其对分割结果的影响。
2) Weighted K-means clustering
加权K均值聚类
4) mean value weighting
均值加权
5) weighted mean
加权均值
1.
On the second power weighted mean of the Dirichlet L-functions;
关于Dirichlet L─函数倒数的二次加权均值
2.
On the distribution of k-th weighted mean of Dirichlet L-functions;
关于Dirichlet L—函数的一个k次加权均值分布
3.
On the second power weighted mean of the inversion of Dirichlet L-functions
Dirichlet L——函数倒数的二次加权均值分布
6) kernel K-means clustering
核K-均值聚类
1.
Aimming at the shortage of Sparse Kernel Principal Component Analysis(SKPCA) in feature extraction,a novel feature extraction method based on the kernel K-means clustering and the SKPCA for speaker recognition is proposed.
针对稀疏核主成分分析方法在特征提取中的不足,提出了一种基于核K-均值聚类的稀疏核主成分分析(Sparse KPCA)的特征提取方法用于说话人识别。
2.
We proposed a novel approach that is based on the kernel K-means clustering and support vector machine(SVM),set up a support vector machine for each two speakers,here kernel K-means clustering is exploited to input speech signal of SVM into a given amount of clusters choosing the effective samples as the input of SVM.
提出了基于核K-均值聚类方法与支持向量机结合的说话人识别方法,为每两个人建立一个支持向量机,对支持向量机输入的语音信号先进行核K-均值聚类,并选取有效样本作为支持向量机的输入,本文提出的聚类方法能够去更好的聚类并约简数据,提高了识别率。
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