Parameter Identifiability in Statistical Machine Learning: A Review
Ran, Zhi-Yong1; Hu, Bao-Gang2
2017-05-01
发表期刊NEURAL COMPUTATION
卷号29期号:5页码:1151-1203
文章类型Review
摘要This review examines the relevance of parameter identifiability for statistical models used in machine learning. In addition to defining main concepts, we address several issues of identifiability closely related to machine learning, showing the advantages and disadvantages of state-of- the-art research and demonstrating recent progress. First, we review criteria for determining the parameter structure of models from the literature. This has three related issues: parameter identifiability, parameter redundancy, and reparameterization. Second, we review the deep influence of identifiability on various aspects of machine learning from theoretical and application viewpoints. In addition to illustrating the utility and influence of identifiability, we emphasize the interplay among identifiability theory, machine learning, mathematical statistics, information theory, optimization theory, information geometry, Riemann geometry, symbolic computation, Bayesian inference, algebraic geometry, and others. Finally, we present a new perspective together with the associated challenges.
关键词Parameter Identifiability Statistical Machine Learning
WOS标题词Science & Technology ; Technology ; Life Sciences & Biomedicine
DOI10.1162/NECO_a_00947
关键词[WOS]NATURAL GRADIENT DESCENT ; MULTILAYER NEURAL-NETWORKS ; SOFT COMMITTEE MACHINES ; INFORMATION CRITERION ; STRUCTURAL IDENTIFIABILITY ; COMPARTMENTAL-MODELS ; COMPUTER ALGEBRA ; LIKELIHOOD RATIO ; HIDDEN UNITS ; SINGULARITIES
收录类别SCI ; SSCi
语种英语
项目资助者NSFC(61273196 ; 61620106003)
WOS研究方向Computer Science ; Neurosciences & Neurology
WOS类目Computer Science, Artificial Intelligence ; Neurosciences
WOS记录号WOS:000399679500001
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文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/15089
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, Chongqing Key Lab Computat Intelligence, Sch Comp Sci & Technol, Chongqing 400065, Peoples R China
2.Chinese Acad Sci, Inst Automat, NLPR & LIAMA, Beijing 100190, Peoples R China
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Ran, Zhi-Yong,Hu, Bao-Gang. Parameter Identifiability in Statistical Machine Learning: A Review[J]. NEURAL COMPUTATION,2017,29(5):1151-1203.
APA Ran, Zhi-Yong,&Hu, Bao-Gang.(2017).Parameter Identifiability in Statistical Machine Learning: A Review.NEURAL COMPUTATION,29(5),1151-1203.
MLA Ran, Zhi-Yong,et al."Parameter Identifiability in Statistical Machine Learning: A Review".NEURAL COMPUTATION 29.5(2017):1151-1203.
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