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Dm-KDE： dynamical kernel density estimation by sequences of KDE estimators with fixed number of components over data streams

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Frontiers of Computer Science 2014年第4期8卷 563-580页

作者： Min XU Hisao ISHIBUCHI Xin GU Shitong WANG School of digital Media Jiangnan University Wuxi 214122 China School of Internet of Things Technology Wuxi Institute of Technology Wuxi 214121 China Department of Computer Science Osaka Prefecture University Osaka 599-8531 Japan

In many data stream mining applications, traditional density estimation methods such as kemel density estimation, reduced set density estimation can not be applied to the density estimation of data streams because of their high computational burden, processing time and intensive memory allocation requirement. In order to reduce the time and space complexity, a novel density estimation method Dm-KDE over data streams based on the proposed algorithm m-KDE which can be used to design a KDE estimator with the fixed number of kernel components for a dataset is proposed. In this method, Dm-KDE sequence entries are created by algorithm m-KDE instead of all kemels obtained from other density estimation methods. In order to further reduce the storage space, Dm-KDE sequence entries can be merged by calculating their KL divergences. Finally, the probability density functions over arbitrary time or entire time can be estimated through the obtained estimation model. In contrast to the state-of-the-art algorithm SOMKE, the distinctive advantage of the proposed algorithm Dm-KDE exists in that it can achieve the same accuracy with much less fixed number of kernel components such that it is suitable for the scenarios where higher on-line computation about the kernel density estimation over data streams is required. We compare Dm-KDE with SOMKE and M-kernel in terms of density estimation accuracy and running time for various stationary datasets. We also apply Dm-KDE to evolving data streams. Experimental results illustrate the effectiveness of the pro- posed method.

关键词： kernel density estimation kullback-leibler di- vergence data streams kernel width time and space complexity

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BAYESIAN LOCAL INFLUENCE ASSESSMENTS IN A GROWTH CURVE MODEL WITH GENERAL COVARIANCE STRUCTURE

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Acta Mathematica Scientia 2000年第4期20卷 563-570页

作者：白鹏费宇 Department of Statistics Yunnan University Kunming 650091 China Institute of Applied Mathematics Yunnan Province Kunming 650091 China

The objective of this paper is to present a Bayesian approach based on kullback- leibler divergence for assessing local influence in a growth curve model with general co- variance structure. Under certain prior distribution assumption, the kullback-leibler di- vergence is used to measure the influence of some minor perturbation on the posterior distribution of unknown parameter. This leads to the diagnostic statistic for detecting which response is locally influential. As an application, the common covariance-weighted perturbation scheme is thoroughly considered.

关键词： Growth curve model prior and posterior distribution kullback-leibler di- vergence Bayesianω-model curvature

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