Panel data combine cross-section data and time series data. If the cross-section is locations, there is a need to check the correlation among locations. ρ and λ are parameters in generalized spatial model to cover e...Panel data combine cross-section data and time series data. If the cross-section is locations, there is a need to check the correlation among locations. ρ and λ are parameters in generalized spatial model to cover effect of correlation between locations. Value of ρ or λ will influence the goodness of fit model, so it is important to make parameter estimation. The effect of another location is covered by making contiguity matrix until it gets spatial weighted matrix (W). There are some types of W—uniform W, binary W, kernel Gaussian W and some W from real case of economics condition or transportation condition from locations. This study is aimed to compare uniform W and kernel Gaussian W in spatial panel data model using RMSE value. The result of analysis showed that uniform weight had RMSE value less than kernel Gaussian model. Uniform W had stabil value for all the combinations.展开更多
Considering a class of operators which include fractional integrals related to operators with Gaussian kernel bounds, the fractional integral operators with rough kernels and fractional maximal operators with rough ke...Considering a class of operators which include fractional integrals related to operators with Gaussian kernel bounds, the fractional integral operators with rough kernels and fractional maximal operators with rough kernels as special cases, we prove that if these operators are bounded on weighted Lebesgue spaces and satisfy some local pointwise control, then these operators and the commutators of these operators with a BMO functions are also bounded on generalized weighted Morrey spaces.展开更多
针对局部线性嵌入(Locally Linear Embedding,LLE)算法在挖掘数据结构时未考虑特征权重且仅局限于数据的线性拟合关系,导致特征提取效果不佳的问题,提出一种基于熵权距离的图正则局部线性嵌入(Graph Regular Local Linear Embedding Alg...针对局部线性嵌入(Locally Linear Embedding,LLE)算法在挖掘数据结构时未考虑特征权重且仅局限于数据的线性拟合关系,导致特征提取效果不佳的问题,提出一种基于熵权距离的图正则局部线性嵌入(Graph Regular Local Linear Embedding Algorithm Based on Entropy Weight Distance,EWD-GLLE)算法。首先,采用信息熵加权的余弦距离划分样本邻域,减小不重要特征对邻域划分的影响,提高了邻域划分的准确性;然后,利用融合热核权重与余弦权重的拉普拉斯图约束低维嵌入,以保留更多的原始数据信息,进而提取到更显著的特征。在两种轴承数据集上的实验结果表明:EWD-GLLE算法的特征提取性能明显优于LLE、LTSA、LDA算法。展开更多
文摘Panel data combine cross-section data and time series data. If the cross-section is locations, there is a need to check the correlation among locations. ρ and λ are parameters in generalized spatial model to cover effect of correlation between locations. Value of ρ or λ will influence the goodness of fit model, so it is important to make parameter estimation. The effect of another location is covered by making contiguity matrix until it gets spatial weighted matrix (W). There are some types of W—uniform W, binary W, kernel Gaussian W and some W from real case of economics condition or transportation condition from locations. This study is aimed to compare uniform W and kernel Gaussian W in spatial panel data model using RMSE value. The result of analysis showed that uniform weight had RMSE value less than kernel Gaussian model. Uniform W had stabil value for all the combinations.
文摘Considering a class of operators which include fractional integrals related to operators with Gaussian kernel bounds, the fractional integral operators with rough kernels and fractional maximal operators with rough kernels as special cases, we prove that if these operators are bounded on weighted Lebesgue spaces and satisfy some local pointwise control, then these operators and the commutators of these operators with a BMO functions are also bounded on generalized weighted Morrey spaces.
文摘针对局部线性嵌入(Locally Linear Embedding,LLE)算法在挖掘数据结构时未考虑特征权重且仅局限于数据的线性拟合关系,导致特征提取效果不佳的问题,提出一种基于熵权距离的图正则局部线性嵌入(Graph Regular Local Linear Embedding Algorithm Based on Entropy Weight Distance,EWD-GLLE)算法。首先,采用信息熵加权的余弦距离划分样本邻域,减小不重要特征对邻域划分的影响,提高了邻域划分的准确性;然后,利用融合热核权重与余弦权重的拉普拉斯图约束低维嵌入,以保留更多的原始数据信息,进而提取到更显著的特征。在两种轴承数据集上的实验结果表明:EWD-GLLE算法的特征提取性能明显优于LLE、LTSA、LDA算法。
