Minimum noiseless description length (MNDL) thresholding

被引：2
|
作者：
Fakhrzadeh, Azadeh [1 ]
Beheshti, Soosan [1 ]
机构：
[1] Ryerson Univ, Dept Elect & Comp Engn, Toronto, ON, Canada
来源：
2007 IEEE SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE IN IMAGE AND SIGNAL PROCESSING | 2007年
关键词：
D O I：
10.1109/CIISP.2007.369308
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, a new thresholding approach for data denoising is presented. The approach is based minimum noiseless description length (MNDL), a new method for optimum sub-space selection in data representation. By using the observed noisy data, this information theoretic approach provides the optimum threshold that minimizes the description length of the noiseless signal. Comparison of the new method with the existing thresholding methods is provided.
引用
收藏
页码：146 / 150
页数：5
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