Maximum Mutual Information
Maximum Mutual Information is a model selection criterion that belongs to the family of information-theoretic methods, distinguished by its principle of maximizing the mutual information between input features and latent representations to achieve optimal predictive relevance.
544
Publications
30.1K
Citations
1.4K
Authors
539
Institutions
Publications per year
2017–2026
180
Variants
Mmi
Parents
544
Fast Binary Feature Selection with Conditional Mutual Information
François Fleuret · 2004 · 836 citations
Multi-modality image registration by maximization of mutual information
Frederik Maes, André Collignon, Dirk Vandermeulen et al. · 1996 · 804 citations
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539
University of Cambridge
Cambridge, United Kingdom
13 h-index
32 publications
1.4K citations

Centre National de la Recherche Scientifique
Paris, France
12 h-index
16 publications
439 citations
IBM Research - Thomas J. Watson Research Center
Yorktown Heights, United States
11 h-index
31 publications
6.9K citations
University of Illinois Urbana-Champaign
Urbana, United States
11 h-index
32 publications
1.5K citations
UCLouvain
Louvain-la-Neuve, Belgium
10 h-index
25 publications
2K citations

Johns Hopkins University
Baltimore, United States
10 h-index
24 publications
988 citations

Shanghai Jiao Tong University
Shanghai, China
8 h-index
25 publications
1.6K citations
Stanford University
Stanford, United States
7 h-index
12 publications
280 citations
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20 publications · 1.2K citations
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Repository · 25 publications · 1.3K citations
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19 publications · 806 citations
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14 publications · 1.4K citations
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10 publications · 830 citations
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