In ultra-high-dimensional data, it is common for the response variable to be multi-classified. Therefore, this paper proposes a model-free screening method for variables whose response variable is multi-classified fro...In ultra-high-dimensional data, it is common for the response variable to be multi-classified. Therefore, this paper proposes a model-free screening method for variables whose response variable is multi-classified from the point of view of introducing Jensen-Shannon divergence to measure the importance of covariates. The idea of the method is to calculate the Jensen-Shannon divergence between the conditional probability distribution of the covariates on a given response variable and the unconditional probability distribution of the covariates, and then use the probabilities of the response variables as weights to calculate the weighted Jensen-Shannon divergence, where a larger weighted Jensen-Shannon divergence means that the covariates are more important. Additionally, we also investigated an adapted version of the method, which is to measure the relationship between the covariates and the response variable using the weighted Jensen-Shannon divergence adjusted by the logarithmic factor of the number of categories when the number of categories in each covariate varies. Then, through both theoretical and simulation experiments, it was demonstrated that the proposed methods have sure screening and ranking consistency properties. Finally, the results from simulation and real-dataset experiments show that in feature screening, the proposed methods investigated are robust in performance and faster in computational speed compared with an existing method.展开更多
Health monitoring data or the data about infectious diseases such as COVID-19 may need to be constantly updated and dynamically released,but they may contain user's sensitive information.Thus,how to preserve the u...Health monitoring data or the data about infectious diseases such as COVID-19 may need to be constantly updated and dynamically released,but they may contain user's sensitive information.Thus,how to preserve the user's privacy before their release is critically important yet challenging.Differential Privacy(DP)is well-known to provide effective privacy protection,and thus the dynamic DP preserving data release was designed to publish a histogram to meet DP guarantee.Unfortunately,this scheme may result in high cumulative errors and lower the data availability.To address this problem,in this paper,we apply Jensen-Shannon(JS)divergence to design the OPTICS(Ordering Points To Identify The Clustering Structure)scheme.It uses JS divergence to measure the difference between the updated data set at the current release time and private data set at the previous release time.By comparing the difference with a threshold,only when the difference is greater than the threshold,can we apply OPTICS to publish DP protected data sets.Our experimental results show that the absolute errors and average relative errors are significantly lower than those existing works.展开更多
In this paper, the idea of interest coverage is provided to form clusters in sensor network, which mean that the distance among data trends gathered by neighbor sensors is so small that, in some period, those sensors ...In this paper, the idea of interest coverage is provided to form clusters in sensor network, which mean that the distance among data trends gathered by neighbor sensors is so small that, in some period, those sensors can be clustered, and certain sensor can be used to replace the cluster to form the virtual sensor network topology. In detail, the Jensen-Shannon Divergence (JSD) is used to characterize the distance among different distributions which represent the data trend of sensors. Then, based on JSD, a hierarchical clustering algorithm is provided to form the virtual sensor network topology. Simulation shows that the proposed approach gains more than 50% energy saving than Sta- tistical Aggregation Methods (SAM) which transmitted data gathered by sensor only when the differ- ence among data exceed certain threshold.展开更多
文摘In ultra-high-dimensional data, it is common for the response variable to be multi-classified. Therefore, this paper proposes a model-free screening method for variables whose response variable is multi-classified from the point of view of introducing Jensen-Shannon divergence to measure the importance of covariates. The idea of the method is to calculate the Jensen-Shannon divergence between the conditional probability distribution of the covariates on a given response variable and the unconditional probability distribution of the covariates, and then use the probabilities of the response variables as weights to calculate the weighted Jensen-Shannon divergence, where a larger weighted Jensen-Shannon divergence means that the covariates are more important. Additionally, we also investigated an adapted version of the method, which is to measure the relationship between the covariates and the response variable using the weighted Jensen-Shannon divergence adjusted by the logarithmic factor of the number of categories when the number of categories in each covariate varies. Then, through both theoretical and simulation experiments, it was demonstrated that the proposed methods have sure screening and ranking consistency properties. Finally, the results from simulation and real-dataset experiments show that in feature screening, the proposed methods investigated are robust in performance and faster in computational speed compared with an existing method.
基金supported in part by National Natural Science Foundation of China(No.61672106)in part by Natural Science Foundation of Beijing,China(L192023)in part by the project of promoting the Classified Development of Beijing Information Science and Technology University(No.5112211038,5112211039)。
文摘Health monitoring data or the data about infectious diseases such as COVID-19 may need to be constantly updated and dynamically released,but they may contain user's sensitive information.Thus,how to preserve the user's privacy before their release is critically important yet challenging.Differential Privacy(DP)is well-known to provide effective privacy protection,and thus the dynamic DP preserving data release was designed to publish a histogram to meet DP guarantee.Unfortunately,this scheme may result in high cumulative errors and lower the data availability.To address this problem,in this paper,we apply Jensen-Shannon(JS)divergence to design the OPTICS(Ordering Points To Identify The Clustering Structure)scheme.It uses JS divergence to measure the difference between the updated data set at the current release time and private data set at the previous release time.By comparing the difference with a threshold,only when the difference is greater than the threshold,can we apply OPTICS to publish DP protected data sets.Our experimental results show that the absolute errors and average relative errors are significantly lower than those existing works.
基金the National Natural Science Foundation of China (No.60472067)Jiangsu Education Bureau (5KJB510091)State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications (BUPT).
文摘In this paper, the idea of interest coverage is provided to form clusters in sensor network, which mean that the distance among data trends gathered by neighbor sensors is so small that, in some period, those sensors can be clustered, and certain sensor can be used to replace the cluster to form the virtual sensor network topology. In detail, the Jensen-Shannon Divergence (JSD) is used to characterize the distance among different distributions which represent the data trend of sensors. Then, based on JSD, a hierarchical clustering algorithm is provided to form the virtual sensor network topology. Simulation shows that the proposed approach gains more than 50% energy saving than Sta- tistical Aggregation Methods (SAM) which transmitted data gathered by sensor only when the differ- ence among data exceed certain threshold.
