Least square support vector regression(LSSVR)is a method for function approximation,whose solutions are typically non-sparse,which limits its application especially in some occasions of fast prediction.In this paper,a...Least square support vector regression(LSSVR)is a method for function approximation,whose solutions are typically non-sparse,which limits its application especially in some occasions of fast prediction.In this paper,a sparse algorithm for adaptive pruning LSSVR algorithm based on global representative point ranking(GRPR-AP-LSSVR)is proposed.At first,the global representative point ranking(GRPR)algorithm is given,and relevant data analysis experiment is implemented which depicts the importance ranking of data points.Furthermore,the pruning strategy of removing two samples in the decremental learning procedure is designed to accelerate the training speed and ensure the sparsity.The removed data points are utilized to test the temporary learning model which ensures the regression accuracy.Finally,the proposed algorithm is verified on artificial datasets and UCI regression datasets,and experimental results indicate that,compared with several benchmark algorithms,the GRPR-AP-LSSVR algorithm has excellent sparsity and prediction speed without impairing the generalization performance.展开更多
针对Science发表的密度峰值聚类(Density peaks clustering,DPC)算法及其改进算法效率不高的缺陷,提出一种相对邻域和剪枝策略优化的密度峰值聚类(Relative neighborhood and pruning strategy optimized DPC,RP-DPC)算法.DPC聚类算法...针对Science发表的密度峰值聚类(Density peaks clustering,DPC)算法及其改进算法效率不高的缺陷,提出一种相对邻域和剪枝策略优化的密度峰值聚类(Relative neighborhood and pruning strategy optimized DPC,RP-DPC)算法.DPC聚类算法主要有两个阶段:聚类中心点的确定和非聚类中心点样本的类簇分配,并且时间复杂度集中在第1个阶段,因此RP-DPC算法针对该阶段做出改进研究.RP-DPC算法去掉了DPC算法预先计算距离矩阵的步骤,首先利用相对距离将样本映射到相对邻域中,再从相对邻域来计算各样本的密度,从而缩小各样本距离计算及密度统计的范围;然后在计算各样本的δ值时加入剪枝策略,将大量被剪枝样本δ值的计算范围从样本集缩小至邻域以内,极大地提高了算法的效率.理论分析和在人工数据集及UCI数据集的对比实验均表明,与DPC算法及其改进算法相比,RP-DPC算法在保证聚类质量的同时可以实现有效的时间性能提升.展开更多
基金supported by the Science and Technology on Space Intelligent Control Laboratory for National Defense(KGJZDSYS-2018-08)。
文摘Least square support vector regression(LSSVR)is a method for function approximation,whose solutions are typically non-sparse,which limits its application especially in some occasions of fast prediction.In this paper,a sparse algorithm for adaptive pruning LSSVR algorithm based on global representative point ranking(GRPR-AP-LSSVR)is proposed.At first,the global representative point ranking(GRPR)algorithm is given,and relevant data analysis experiment is implemented which depicts the importance ranking of data points.Furthermore,the pruning strategy of removing two samples in the decremental learning procedure is designed to accelerate the training speed and ensure the sparsity.The removed data points are utilized to test the temporary learning model which ensures the regression accuracy.Finally,the proposed algorithm is verified on artificial datasets and UCI regression datasets,and experimental results indicate that,compared with several benchmark algorithms,the GRPR-AP-LSSVR algorithm has excellent sparsity and prediction speed without impairing the generalization performance.
文摘针对Science发表的密度峰值聚类(Density peaks clustering,DPC)算法及其改进算法效率不高的缺陷,提出一种相对邻域和剪枝策略优化的密度峰值聚类(Relative neighborhood and pruning strategy optimized DPC,RP-DPC)算法.DPC聚类算法主要有两个阶段:聚类中心点的确定和非聚类中心点样本的类簇分配,并且时间复杂度集中在第1个阶段,因此RP-DPC算法针对该阶段做出改进研究.RP-DPC算法去掉了DPC算法预先计算距离矩阵的步骤,首先利用相对距离将样本映射到相对邻域中,再从相对邻域来计算各样本的密度,从而缩小各样本距离计算及密度统计的范围;然后在计算各样本的δ值时加入剪枝策略,将大量被剪枝样本δ值的计算范围从样本集缩小至邻域以内,极大地提高了算法的效率.理论分析和在人工数据集及UCI数据集的对比实验均表明,与DPC算法及其改进算法相比,RP-DPC算法在保证聚类质量的同时可以实现有效的时间性能提升.
