Determining parameter identifiability from the optimization theory framework: A Kullback-Leibler divergence approach
文献类型：期刊论文
| 作者 | Ran, Zhi-Yong1; Hu, Bao-Gang
|
| 刊名 | NEUROCOMPUTING
 |
| 出版日期 | 2014-10-22 |
| 卷号 | 142页码:307-317 |
| 关键词 | Identifiability Optimization theory Kullback-Leibler divergence Hessian matrix Jacobian matrix |
| 英文摘要 | This paper reports an extension of the existing investigations on determining identifiability of statistical parameter models. By making use of the Kullback-Leibler divergence (KLD) in information theory, we cast the identifiability problem into the optimization theory framework. This is the first work that studies the identifiability problem from the optimization theory perspective which leads to connections in many areas of scientific research, e.g., identifiability theory, information theory and optimization theory. Within this new framework, we derive identifiability criteria according to the types of models. First, by formulating the identifiability problem of unconstrained parameter models as an unconstrained optimization problem, we derive identifiability criteria by checking the rank of the Hessian matrix of KLD. The resulting theorems extend the existing approaches and work in arbitrary statistical models. Second, by formulating the identifiability problem of parameter-constrained models as a constrained optimization problem, we derive a novel criterion which has a clear algebraic and geometric interpretation. Further, we discuss the pros/cons of the new framework from both theoretical and application viewpoints. Several model examples from the literature are presented to examine their identifiability property. (C) 2014 Elsevier B.V. All rights reserved. |
| WOS标题词 | Science & Technology ; Technology |
| 类目[WOS] | Computer Science, Artificial Intelligence |
| 研究领域[WOS] | Computer Science |
| 关键词[WOS] | NEURAL-NETWORK MODEL ; STRUCTURAL IDENTIFIABILITY ; INFORMATION CRITERION ; LEARNING MACHINES ; IDENTIFICATION ; CONSTRAINTS ; REGULARITY ; DYNAMICS |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000340341400033 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/2872]  |
| 专题 | 自动化研究所_模式识别国家重点实验室_多媒体计算与图形学团队 |
| 作者单位 | 1.Chinese Acad Sci, NLPR, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Inst Automat, LIAMA, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Ran, Zhi-Yong,Hu, Bao-Gang. Determining parameter identifiability from the optimization theory framework: A Kullback-Leibler divergence approach[J]. NEUROCOMPUTING,2014,142:307-317. |
| APA | Ran, Zhi-Yong,&Hu, Bao-Gang.(2014).Determining parameter identifiability from the optimization theory framework: A Kullback-Leibler divergence approach.NEUROCOMPUTING,142,307-317. |
| MLA | Ran, Zhi-Yong,et al."Determining parameter identifiability from the optimization theory framework: A Kullback-Leibler divergence approach".NEUROCOMPUTING 142(2014):307-317. |
入库方式： OAI收割
来源：自动化研究所
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