An identifying function approach for determining parameter structure of statistical learning machines
Ran, Zhi-Yong1; Hu, Bao-Gang2,3
2015-08-25
发表期刊NEUROCOMPUTING
卷号162页码:209-217
文章类型Article
摘要This paper presents an identifying function (IF) approach for determining parameter structure of statistical learning machines (SLMs). This involves studying three related aspects: structural identifiability (SI), parameter redundancy (PR) and reparameterization. Firstly, by employing the Rank Theorem in Riemann geometry, we derive an efficient identifiability criterion by calculating the rank of the derivative matrix (DM) of IF. Secondly, we extend the previous concept of IF to local IF (LIF) for examining local parameter structure of SLMs, and prove that the Kullback-Leibler divergence (KLD) is such a proper LIF, thus relating the LIF approach to several existing criteria. Lastly, an analytical approach for solving minimal reparameterization in parameter-redundant models is established. The dimensionality of the minimal reparameterization can be used to characterize the intrinsic parameter dimensionality of model. We compare the IF approach with existing criteria and discuss its pros/cons from theoretical and application viewpoints. Several model examples from the literature are presented to study their parameter structure. (C) 2015 Elsevier B.V. All rights reserved.
关键词Identifying Function Structural Identifiability Statistical Learning Machine Kullback-leibler Divergence Parameter Redundancy Reparameterization
WOS标题词Science & Technology ; Technology
关键词[WOS]NEURAL-NETWORK MODEL ; INFORMATION CRITERION ; IDENTIFIABILITY ; IDENTIFICATION
收录类别SCI
语种英语
WOS研究方向Computer Science
WOS类目Computer Science, Artificial Intelligence
WOS记录号WOS:000356125200021
引用统计
被引频次:1[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/7936
专题模式识别国家重点实验室_多媒体计算与图形学
作者单位1.Chongqing Univ Posts & Telecommun, Sch Comp Sci & Technol, Chongqing 400065, Peoples R China
2.Chinese Acad Sci, Inst Automat, NLPR, Beijing 100190, Peoples R China
3.Chinese Acad Sci, Inst Automat, LIAMA, Beijing 100190, Peoples R China
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Ran, Zhi-Yong,Hu, Bao-Gang. An identifying function approach for determining parameter structure of statistical learning machines[J]. NEUROCOMPUTING,2015,162:209-217.
APA Ran, Zhi-Yong,&Hu, Bao-Gang.(2015).An identifying function approach for determining parameter structure of statistical learning machines.NEUROCOMPUTING,162,209-217.
MLA Ran, Zhi-Yong,et al."An identifying function approach for determining parameter structure of statistical learning machines".NEUROCOMPUTING 162(2015):209-217.
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