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
  • [31] Fritzke B., A growing neural gas network learns topologies, Advances in Neural Information Processing Systems, pp. 625-632, (1995)
  • [32] Fritzke B., Growing cell structures-a self-organizing network for unsupervised and supervised learning, Neural Networks, 7, 9, pp. 1441-1460, (1994)
  • [33] MacQueen J., Some methods for classification and analysis of multivariate observations, Proc. of the 5th Berkeley Symp. on Mathematical Statistics and Probability, 1, 14, pp. 281-297, (1967)
  • [34] Gray R., Vector quantization, IEEE ASSP Magazine, 1, 2, pp. 4-29, (1984)
  • [35] Robbins H., Monro S., A stochastic approximation method, Herbert Robbins Selected Papers, pp. 102-109, (1985)
  • [36] Zador P., Asymptotic quantization error of continuous signals and the quantization dimension, IEEE Trans. on Information Theory, 28, 2, pp. 139-149, (1982)
  • [37] Terrell G.R., Scott D.W., Variable kernel density estimation, The Annals of Statistics, 20, 3, pp. 1236-1265, (1992)
  • [38] Burges C.J.C., A tutorial on support vector machines for pattern recognition, Data Mining and Knowledge Discovery, 2, 2, pp. 121-167, (1998)
  • [39] Shen F., Hasegawa O., An incremental network for on-line unsupervised classification and topology learning, Neural Networks, 19, 19, pp. 90-106, (2006)
  • [40] Shen F., Ogura T., Hasegawa O., An enhanced self-organizing incremental neural network for online unsupervised learning, Neural Networks, 20, 8, pp. 893-903, (2007)
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