Adaptive Online Kernel Density Estimation Method

被引：0
|
作者：
Deng Q.-L. [1 ,2 ]
Qiu T.-Y. [1 ,2 ]
Shen F.-R. [1 ,2 ]
Zhao J.-X. [1 ,2 ]
机构：
[1] State Key Laboratory for Novel Software Technology (Nanjing University), Nanjing
[2] Department of Computer Science and Technology, Nanjing University, Nanjing
来源：
Ruan Jian Xue Bao/Journal of Software | 2020年 / 31卷 / 04期
基金：
中国国家自然科学基金;
关键词：
Competitive learning; Data stream; Density estimation; Gaussian mixture model; Online learning;
D O I：
10.13328/j.cnki.jos.005674
中图分类号：
学科分类号：
摘要：
Based on observed data, density estimation is the construction of an estimate of an unobservable underlying probability density function. With the development of data collection technology, real-time streaming data becomes the main subject of many related tasks. It has the properties of that high throughput, high generation speed, and the underlying distribution of data may change over time. However, for the traditional density estimation algorithms, parametric methods make unrealistic assumptions on the estimated density function while non-parametric ones suffer from the unacceptable time and space complexity. Therefore, neither parametric nor non-parametric ones could scale well to meet the requirements of streaming data environment. In this study, based on the analysis of the learning strategy in competitive learning, it is proposed a novel online density estimation algorithm to accomplish the task of density estimation for such streaming data. And it is also pointed out that it has pretty close relationship with the Gaussian mixture model. Finally, the proposed algorithm is compared with the existing density estimation algorithms. The experimental results show that the proposed algorithm could obtain better estimates compared with the existing online algorithm, and also get comparable estimation performance compared with state-of-the-art offline density estimation algorithms. © Copyright 2020, Institute of Software, the Chinese Academy of Sciences. All rights reserved.
引用
收藏
页码：1173 / 1188
页数：15
相关论文
共 45 条
  • [1] Latecki L.J., Lazarevic A., Pokrajac D., Outlier detection with kernel density functions, Proc. of the Int'l Workshop on Machine Learning and Data Mining in Pattern Recognition, pp. 61-75, (2007)
  • [2] Laxhammar R., Falkman G., Sviestins E., Anomaly detection in sea traffic-a comparison of the gaussian mixture model and the kernel density estimator, Proc. of the 12th Int'l Conf. on Information Fusion, pp. 756-763, (2009)
  • [3] Costa B.S.J., Angelov P.P., Guedes L.A., Real-time fault detection using recursive density estimation, Journal of Control, Automation and Electrical Systems, 25, 4, pp. 428-437, (2014)
  • [4] Chen H., Meer P., Robust computer vision through kernel density estimation, Proc. of the 2002 European Conf. on Computer Vision, pp. 236-250, (2002)
  • [5] Yang C., Duraiswami R., Gumerov N.A., Davis L., Improved fast Gauss transform and efficient kernel density estimation, Proc. of the IEEE Int'l Conf. on Computer Vision, pp. 664-671, (2003)
  • [6] Elgammal A., Duraiswami R., Harwood D., Et al., Background and foreground modeling using nonparametric kernel density estimation for visual surveillance, Proc. of the IEEE, pp. 1151-1163, (2002)
  • [7] Mittal A., Paragios N., Motion-based background subtraction using adaptive kernel density estimation, Proc. of the 2004 IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, pp. 302-309, (2004)
  • [8] Zivkovic Z., Ferdinand V.D.H., Efficient adaptive density estimation per image pixel for the task of background subtraction, Pattern Recognition Letters, 27, 7, pp. 773-780, (2006)
  • [9] Nakaya T., Yano K., Visualising crime clusters in a space-time cube: An exploratory data-analysis approach using space-time kernel density estimation and scan statistics, Trans. in GIS, 14, 3, pp. 223-239, (2010)
  • [10] Lampe O.D., Hauser H., Interactive visualization of streaming data with kernel density estimation, Proc. of the 2011 IEEE Pacific Visualization Symp, pp. 171-178, (2011)
12345