CASIA OpenIR  > 模式识别国家重点实验室  > 多媒体计算与图形学
An identifying function approach for determining parameter structure of statistical learning machines
Ran, Zhi-Yong1; Hu, Bao-Gang2,3
Source PublicationNEUROCOMPUTING
2015-08-25
Volume162Pages:209-217
SubtypeArticle
AbstractThis 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.
KeywordIdentifying Function Structural Identifiability Statistical Learning Machine Kullback-leibler Divergence Parameter Redundancy Reparameterization
WOS HeadingsScience & Technology ; Technology
WOS KeywordNEURAL-NETWORK MODEL ; INFORMATION CRITERION ; IDENTIFIABILITY ; IDENTIFICATION
Indexed BySCI
Language英语
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000356125200021
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/7936
Collection模式识别国家重点实验室_多媒体计算与图形学
Affiliation1.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
Recommended Citation
GB/T 7714
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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