为了解决密度峰值聚类(Density Peaks Clustering,DPC)算法截断距离选取困难以及需人工选择聚类中心的问题,提出结合密度峰值和樽海鞘群搜索的数据聚类算法。在信息熵中引入密度测度,提出密度估计信息熵,并以此建立目标函数;利用樽海鞘...为了解决密度峰值聚类(Density Peaks Clustering,DPC)算法截断距离选取困难以及需人工选择聚类中心的问题,提出结合密度峰值和樽海鞘群搜索的数据聚类算法。在信息熵中引入密度测度,提出密度估计信息熵,并以此建立目标函数;利用樽海鞘群搜索的寻优机制,得到最优截断距离参数;依据归一化局部密度和相对距离乘积,自适应选取聚类中心。为了验证所提算法的可行性和有效性,以8个典型人工合成数据集和2个UCI(University of California Irvine)真实数据集作为测试数据,与其他6种聚类算法相比较。研究结果表明,所提算法能有效解决传统DPC算法聚类中心选择的问题,避免了人工选取的主观性,克服了截断距离参数选取困难的问题。相对于比较算法,所提算法具有更强的全局搜索能力、更高的稳定性和更好的聚类效果。展开更多
Based on the data-cutoff method,we study quantile regression in linear models,where the noise process is of Ornstein-Uhlenbeck type with possible jumps.In single-level quantile regression,we allow the noise process to...Based on the data-cutoff method,we study quantile regression in linear models,where the noise process is of Ornstein-Uhlenbeck type with possible jumps.In single-level quantile regression,we allow the noise process to be heteroscedastic,while in composite quantile regression,we require that the noise process be homoscedastic so that the slopes are invariant across quantiles.Similar to the independent noise case,the proposed quantile estimators are root-n consistent and asymptotic normal.Furthermore,the adaptive least absolute shrinkage and selection operator(LASSO)is applied for the purpose of variable selection.As a result,the quantile estimators are consistent in variable selection,and the nonzero coefficient estimators enjoy the same asymptotic distribution as their counterparts under the true model.Extensive numerical simulations are conducted to evaluate the performance of the proposed approaches and foreign exchange rate data are analyzed for the illustration purpose.展开更多
文摘为了解决密度峰值聚类(Density Peaks Clustering,DPC)算法截断距离选取困难以及需人工选择聚类中心的问题,提出结合密度峰值和樽海鞘群搜索的数据聚类算法。在信息熵中引入密度测度,提出密度估计信息熵,并以此建立目标函数;利用樽海鞘群搜索的寻优机制,得到最优截断距离参数;依据归一化局部密度和相对距离乘积,自适应选取聚类中心。为了验证所提算法的可行性和有效性,以8个典型人工合成数据集和2个UCI(University of California Irvine)真实数据集作为测试数据,与其他6种聚类算法相比较。研究结果表明,所提算法能有效解决传统DPC算法聚类中心选择的问题,避免了人工选取的主观性,克服了截断距离参数选取困难的问题。相对于比较算法,所提算法具有更强的全局搜索能力、更高的稳定性和更好的聚类效果。
基金supported by National Natural Science Foundation of China(Grant Nos.11801355 and 11971116).
文摘Based on the data-cutoff method,we study quantile regression in linear models,where the noise process is of Ornstein-Uhlenbeck type with possible jumps.In single-level quantile regression,we allow the noise process to be heteroscedastic,while in composite quantile regression,we require that the noise process be homoscedastic so that the slopes are invariant across quantiles.Similar to the independent noise case,the proposed quantile estimators are root-n consistent and asymptotic normal.Furthermore,the adaptive least absolute shrinkage and selection operator(LASSO)is applied for the purpose of variable selection.As a result,the quantile estimators are consistent in variable selection,and the nonzero coefficient estimators enjoy the same asymptotic distribution as their counterparts under the true model.Extensive numerical simulations are conducted to evaluate the performance of the proposed approaches and foreign exchange rate data are analyzed for the illustration purpose.
