密度峰值聚类(clustering by fast search and find of density peaks,简称DPC)是一种基于局部密度和相对距离属性快速寻找聚类中心的有效算法.DPC通过决策图寻找密度峰值作为聚类中心,不需要提前指定类簇数,并可以得到任意形状的簇聚类...密度峰值聚类(clustering by fast search and find of density peaks,简称DPC)是一种基于局部密度和相对距离属性快速寻找聚类中心的有效算法.DPC通过决策图寻找密度峰值作为聚类中心,不需要提前指定类簇数,并可以得到任意形状的簇聚类.但局部密度和相对距离的计算都只是简单依赖基于距离度量的相似度矩阵,所以在复杂数据上DPC聚类结果不尽如人意,特别是当数据分布不均匀、数据维度较高时.另外,DPC算法中局部密度的计算没有统一的度量,根据不同的数据集需要选择不同的度量方式.第三,截断距离dc的度量只考虑数据的全局分布,忽略了数据的局部信息,所以dc的改变会影响聚类的结果,尤其是在小样本数据集上.针对这些弊端,提出一种基于不相似性度量优化的密度峰值聚类算法(optimized density peaks clustering algorithm based on dissimilarity measure,简称DDPC),引入基于块的不相似性度量方法计算相似度矩阵,并基于新的相似度矩阵计算样本的K近邻信息,然后基于样本的K近邻信息重新定义局部密度的度量方法.经典数据集的实验结果表明,基于不相似性度量优化的密度峰值聚类算法优于DPC的优化算法FKNN-DPC和DPC-KNN,可以在密度不均匀以及维度较高的数据集上得到满意的结果;同时统一了局部密度的度量方式,避免了传统DPC算法中截断距离dc对聚类结果的影响.展开更多
In this paper,we give a locally parabolic version of Tb theorem for a class of vector-valued operators with off-diagonal decay in L^(2) and certain quasi-orthogonality on a subspace of L^(2),in which the testing funct...In this paper,we give a locally parabolic version of Tb theorem for a class of vector-valued operators with off-diagonal decay in L^(2) and certain quasi-orthogonality on a subspace of L^(2),in which the testing functions themselves are also vector-valued.As an application,we establish the boundedness of layer potentials related to parabolic operators in divergence form,defined in the upper half-space Rn+2+:={(x,t,λ)∈R^(n+1)×(0,∞)},with uniformly complex elliptic,L^(∞),t,λ-independent coefficients,and satisfying the De Giorgi/Nash estimates.展开更多
Let BH,K = {BH,K(t), t ∈ R+} be a bifractional Brownian motion in Rd. This process is a selfsimilar Gaussian process depending on two parameters H and K and it constitutes a natural generalization of fractional Brown...Let BH,K = {BH,K(t), t ∈ R+} be a bifractional Brownian motion in Rd. This process is a selfsimilar Gaussian process depending on two parameters H and K and it constitutes a natural generalization of fractional Brownian motion (which is obtained for K = 1). The exact Hausdorff measures of the image, graph and the level set of BH,K are investigated. The results extend the corresponding results proved by Talagrand and Xiao for fractional Brownian motion.展开更多
In this paper, we define and study polynomial entropy on an arbitrary subset and local measure theoretic polynomial entropy for any Borel probability measure on a compact metric space,and investigate the relation betw...In this paper, we define and study polynomial entropy on an arbitrary subset and local measure theoretic polynomial entropy for any Borel probability measure on a compact metric space,and investigate the relation between local measure-theoretic polynomial entropy of Borel probability measures and polynomial entropy on an arbitrary subset. Also, we establish a variational principle for polynomial entropy on compact subsets in the context of amenable group actions.展开更多
文摘密度峰值聚类(clustering by fast search and find of density peaks,简称DPC)是一种基于局部密度和相对距离属性快速寻找聚类中心的有效算法.DPC通过决策图寻找密度峰值作为聚类中心,不需要提前指定类簇数,并可以得到任意形状的簇聚类.但局部密度和相对距离的计算都只是简单依赖基于距离度量的相似度矩阵,所以在复杂数据上DPC聚类结果不尽如人意,特别是当数据分布不均匀、数据维度较高时.另外,DPC算法中局部密度的计算没有统一的度量,根据不同的数据集需要选择不同的度量方式.第三,截断距离dc的度量只考虑数据的全局分布,忽略了数据的局部信息,所以dc的改变会影响聚类的结果,尤其是在小样本数据集上.针对这些弊端,提出一种基于不相似性度量优化的密度峰值聚类算法(optimized density peaks clustering algorithm based on dissimilarity measure,简称DDPC),引入基于块的不相似性度量方法计算相似度矩阵,并基于新的相似度矩阵计算样本的K近邻信息,然后基于样本的K近邻信息重新定义局部密度的度量方法.经典数据集的实验结果表明,基于不相似性度量优化的密度峰值聚类算法优于DPC的优化算法FKNN-DPC和DPC-KNN,可以在密度不均匀以及维度较高的数据集上得到满意的结果;同时统一了局部密度的度量方式,避免了传统DPC算法中截断距离dc对聚类结果的影响.
基金Supported by Natural Science Foundation of Jiangsu Province of China(Grant No.BK20220324)Natural Science Research of Jiangsu Higher Education Institutions of China(Grant No.22KJB110016)。
文摘In this paper,we give a locally parabolic version of Tb theorem for a class of vector-valued operators with off-diagonal decay in L^(2) and certain quasi-orthogonality on a subspace of L^(2),in which the testing functions themselves are also vector-valued.As an application,we establish the boundedness of layer potentials related to parabolic operators in divergence form,defined in the upper half-space Rn+2+:={(x,t,λ)∈R^(n+1)×(0,∞)},with uniformly complex elliptic,L^(∞),t,λ-independent coefficients,and satisfying the De Giorgi/Nash estimates.
基金supported by National Natural Science Foundation of China (Grant No.10721091)
文摘Let BH,K = {BH,K(t), t ∈ R+} be a bifractional Brownian motion in Rd. This process is a selfsimilar Gaussian process depending on two parameters H and K and it constitutes a natural generalization of fractional Brownian motion (which is obtained for K = 1). The exact Hausdorff measures of the image, graph and the level set of BH,K are investigated. The results extend the corresponding results proved by Talagrand and Xiao for fractional Brownian motion.
基金supported by Foundation in higher education institutions of He’nan Province,P. R. China(Grant No. 23A110020)National Natural Science Foundation of China (Grant No. 11401363)+4 种基金the Foundation for the Training of Young Key Teachers in Colleges and Universities in He’nan Province,P. R. China (Grant No.2018GGJS134)supported by National Natural Science Foundation of China (Gratn No.11971236)China Postdoctoral Science Foundation (Grant No. 2016M591873)China Postdoctoral Science Special Foundation (Grant No. 2017T100384)funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions。
文摘In this paper, we define and study polynomial entropy on an arbitrary subset and local measure theoretic polynomial entropy for any Borel probability measure on a compact metric space,and investigate the relation between local measure-theoretic polynomial entropy of Borel probability measures and polynomial entropy on an arbitrary subset. Also, we establish a variational principle for polynomial entropy on compact subsets in the context of amenable group actions.
