Clustering under composite generative models
Clustering under composite generative models
复制标题
复合生成模型下的聚类
DOI：
10.1109/ciss.2018.8362256
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
P. Varshney
中科院分区：
文献类型：
--
作者：
Tiexing Wang;Donald J. Bucci;Yingbin Liang;Biao Chen;P. Varshney
研究了基于Kolmogorov-Smirnov（KS）的K-means算法对复合分布数据样本的聚类问题。所有的数据序列都假定是从未知的连续分布中产生的。假设每个分布类的最大类内KS距离小于不同类的最小类间KS距离。分析的收敛性和上界的错误概率的情况下，已知和未知数量的集群。此外，它示出的错误的概率指数衰减的样本数在每个数据序列中趋于无穷大，和错误指数只是一个函数的差的类间和类内KS距离。仿真结果验证了分析的正确性。
This paper studies clustering of data samples generated from composite distributions using the Kolmogorov-Smirnov (KS) based K-means algorithm. All data sequences are assumed to be generated from unknown continuous distributions. The maximum intra-cluster KS distance of each distribution cluster is assumed to be smaller than the minimum inter-cluster KS distance of different clusters. The analysis of convergence and upper bounds on the error probability are provided for both cases with known and unknown number of clusters. Furthermore, it is shown that the probability of error decays exponentially as the number of samples in each data sequence goes to infinity, and the error exponent is only a function of the difference of the inter-cluster and intra-cluster KS distances. The analysis is validated by simulation results.
通过网络进行几何结构的非参数检测
影响因子：
5.4
作者：
Zou, Shaofeng;Liang, Yingbin;Poor, H. Vincent
通讯作者：
Poor, H. Vincent