The Method of Optimal Group Based on the Kernel Density Estimation and the Close Degree

Fei Ye; Xin Wang; Dong Hui Peng; Chuan Hai Jiao

doi:10.4028/www.scientific.net/AMR.989-994.3689

Paper Titles

Modeling Difficult-to-Measure Process Parameters Based on Intrinsic Mode Functions Frequency Spectral Features of Mechanical Vibration and Acoustical Signals
p.3671

Image Retrieval of Self-Adapt Distance Measure Based on SLLE
p.3675

Study on Deep Structure of Extreme Learning Machine (DS-ELM) for Datasets with Noise
p.3679

Design of 2_DOF Simulation Platform for the Testing of Airborne Laser Communication APT System
p.3683

The Method of Optimal Group Based on the Kernel Density Estimation and the Close Degree
p.3689

Research of Frequency-Hopping Communication for Arbitrary Array Based on Adaptive Beam-Forming
p.3693

Research on Fabric Image Acquisition Based on Compressed Sensing
p.3698

Adaptive Equalization with Variable Step Size Algorithm Based on Gradient for HF Channel
p.3702

Joint Estimation of Doppler and Time Delay of Wideband Echoes for LFM Pulse Radar Based on Fractional Correlation in Impulse Noise Environment
p.3706

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 989-994The Method of Optimal Group Based on the Kernel...

The Method of Optimal Group Based on the Kernel Density Estimation and the Close Degree

Abstract:

The optimal group is an important problem of histogram algorithm, and how to confirm group number has not a quantitative rule. So the concept of the close degree is imported to make the close degree between the upper contour line of histogram and the PDF(probability density function) of parameter as the judging criteria of optimal group. With the unknown of the PDF of parameter, the improved kernel density estimation algorithm can pre-select and estimate the PDF. This improved kernel density estimation algorithm combine the selection of fixed window and variable window's width to achieve the window's width automatic adjustment value between the different estimation points based on the sample distribution. In the parameter analysis of radar emitter signal, the algorithm based on improved kernel density estimation and close degree is used to determine optimal group, and the result indicate that this method is effective and can search the optimal group automatically.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 989-994)

Pages:

3689-3692

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.989-994.3689

Citation:

Cite this paper

Online since:

July 2014

Authors:

Fei Ye*, Xin Wang, Dong Hui Peng, Chuan Hai Jiao

Keywords:

Close Degree, Histogram, Kernel Density Estimation, Optimal Group

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] LiuDianRui. A histogram of the optimal grouping problem [J]. Journal of geophysical and chemistry exploration , 1995, (1) : 62-67.

Google Scholar

[2] DengBiao, YuChuanQiang LiTianShi, SuWenBin, PanYangKe. The double window width kernel density estimation algorithm based on the estimation points [J]. Journal of instrument. 2001, 32 (1) : 615-620.

Google Scholar

[3] LiYun, HuangMinghui. The optimal window width choice improvement of Kernel density estimation point by point [J]. Journal of statistics and decision making. 2011, (14) : 28-32.

Google Scholar

[4] YuChuanJiang, GuoXiaoSong ZhangAn, WuHaiCheng. The sliding window width kernel density estimation algorithm based on the estimation points [J]. Journal of texas. 2009, 30 (2) : 231-235.

Google Scholar

[5] Lenk P J. Bayesian semiparametric density estimation and model verification using a logistic-gaussian model[J]. Comput. Graph. Stat, 2003, 12: 548-565.

DOI: 10.1198/1061860032021

Google Scholar