Data with missing values are often obtained using multivariate statistical analyses.It is crucial to study how to estimate parameters and test hypotheses using such data.There exists a step monotone incomplete sample ...Data with missing values are often obtained using multivariate statistical analyses.It is crucial to study how to estimate parameters and test hypotheses using such data.There exists a step monotone incomplete sample as a simple model of data,which includes such missing values.In this study,we derive the asymptotic distribution of the estimator for the correlation matrix and propose a hypothesis testing method for it in a three-step monotone incomplete sample.Further,we investigate the accuracy of our results by numerical simulation.展开更多
Accurate short-term traffic prediction is essential for improving the efficiency of data transmission in low Earth orbit(LEO)satellite networks.However,traffic values may be missing due to collector failures,transmiss...Accurate short-term traffic prediction is essential for improving the efficiency of data transmission in low Earth orbit(LEO)satellite networks.However,traffic values may be missing due to collector failures,transmission errors,and memory failures in complex space environments.Incomplete traffic time series prevent the efficient utilization of data,which can significantly reduce the traffic prediction accuracy.To overcome this problem,we propose a novel spatio-temporal correlation-based incomplete time-series traffic prediction(ITP-ST)model,which consists of two phases:reconstituting incomplete time series by missing data imputation and making traffic prediction based on the reconstructed time series.In the first phase,we propose a novel missing data imputation model based on the improved denoising autoencoder(IDAE-MDI).Specifically,we combine DAE with the Gramian angular summation field(GASF)to establish the temporal correlation between different time intervals and extract the structural patterns from the time series.Taking advantage of the unique spatio-temporal correlation of the LEO satellite network traffic,we focus on improving the missing data initialization method for DAE.In the second phase,we propose a traffic prediction model based on a multi-channel attention convolutional neural network(TP-CACNN)by combining the spatio-temporally correlated traffic of the LEO satellite network.Finally,to achieve the ideal structure of these models,we use the multi-verse optimizer(MVO)algorithm to select the optimal combination of model parameters.Experiments show that the ITP-ST model outperforms the baseline models in terms of traffic prediction accuracy at different data missing rates,which demonstrates the effectiveness of our proposed model.展开更多
在多标签学习中,人工标注标签的主观性和不稳定性往往造成标签缺失,无法形成完备的标签空间,从而对监督学习算法的训练产生误导.标签相关性可在一定程度上弥补缺失标签对算法分类性能造成的不利影响.但缺失标签也会导致对标签相关性的...在多标签学习中,人工标注标签的主观性和不稳定性往往造成标签缺失,无法形成完备的标签空间,从而对监督学习算法的训练产生误导.标签相关性可在一定程度上弥补缺失标签对算法分类性能造成的不利影响.但缺失标签也会导致对标签相关性的估计不准确.针对该问题,提出一种增强标签相关性矩阵的不完备多标签学习(multi-label learning with incomplete labels via augmented label correlation matrix,ML-ALC)方法.首先,通过拉普拉斯映射构造数据的低维流形;然后,使用标签向量计算原始标签相关矩阵;接着,构造一个校正矩阵对原始标签相关矩阵进行增强,并通过回归系数矩阵和增强标签相关性矩阵将原始特征空间和标签空间分别映射到低维流形;最后,经过迭代学习获得优化的回归系数矩阵和增强标签相关性矩阵,并应用于多标签分类.实验结果表明,ML-ALC方法的分类性能优于其他针对缺失标签的多标签分类方法.展开更多
文摘Data with missing values are often obtained using multivariate statistical analyses.It is crucial to study how to estimate parameters and test hypotheses using such data.There exists a step monotone incomplete sample as a simple model of data,which includes such missing values.In this study,we derive the asymptotic distribution of the estimator for the correlation matrix and propose a hypothesis testing method for it in a three-step monotone incomplete sample.Further,we investigate the accuracy of our results by numerical simulation.
文摘Accurate short-term traffic prediction is essential for improving the efficiency of data transmission in low Earth orbit(LEO)satellite networks.However,traffic values may be missing due to collector failures,transmission errors,and memory failures in complex space environments.Incomplete traffic time series prevent the efficient utilization of data,which can significantly reduce the traffic prediction accuracy.To overcome this problem,we propose a novel spatio-temporal correlation-based incomplete time-series traffic prediction(ITP-ST)model,which consists of two phases:reconstituting incomplete time series by missing data imputation and making traffic prediction based on the reconstructed time series.In the first phase,we propose a novel missing data imputation model based on the improved denoising autoencoder(IDAE-MDI).Specifically,we combine DAE with the Gramian angular summation field(GASF)to establish the temporal correlation between different time intervals and extract the structural patterns from the time series.Taking advantage of the unique spatio-temporal correlation of the LEO satellite network traffic,we focus on improving the missing data initialization method for DAE.In the second phase,we propose a traffic prediction model based on a multi-channel attention convolutional neural network(TP-CACNN)by combining the spatio-temporally correlated traffic of the LEO satellite network.Finally,to achieve the ideal structure of these models,we use the multi-verse optimizer(MVO)algorithm to select the optimal combination of model parameters.Experiments show that the ITP-ST model outperforms the baseline models in terms of traffic prediction accuracy at different data missing rates,which demonstrates the effectiveness of our proposed model.
文摘在多标签学习中,人工标注标签的主观性和不稳定性往往造成标签缺失,无法形成完备的标签空间,从而对监督学习算法的训练产生误导.标签相关性可在一定程度上弥补缺失标签对算法分类性能造成的不利影响.但缺失标签也会导致对标签相关性的估计不准确.针对该问题,提出一种增强标签相关性矩阵的不完备多标签学习(multi-label learning with incomplete labels via augmented label correlation matrix,ML-ALC)方法.首先,通过拉普拉斯映射构造数据的低维流形;然后,使用标签向量计算原始标签相关矩阵;接着,构造一个校正矩阵对原始标签相关矩阵进行增强,并通过回归系数矩阵和增强标签相关性矩阵将原始特征空间和标签空间分别映射到低维流形;最后,经过迭代学习获得优化的回归系数矩阵和增强标签相关性矩阵,并应用于多标签分类.实验结果表明,ML-ALC方法的分类性能优于其他针对缺失标签的多标签分类方法.
