Abstract:
: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.
journal_name
Neural Computjournal_title
Neural computationauthors
Ran ZY,Hu BGdoi
10.1162/NECO_a_00947subject
Has Abstractpub_date
2017-05-01 00:00:00pages
1151-1203issue
5eissn
0899-7667issn
1530-888Xjournal_volume
29pub_type
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