Abstract
In rate-distortion (RD) problems one seeks reduced representations of a source that meet a target distortion constraint. Such optimal representations undergo topological transitions at some critical rate values, when their cardinality or dimensionality change. We study the convergence time of the Arimoto-Blahut alternating projection algorithms, used to solve such problems, near those critical points, both for the ratedistortion and information bottleneck settings. We argue that they suffer from critical slowing down - a diverging number of iterations for convergence - near the critical points. This phenomenon can have theoretical and practical implications for both machine learning and data compression problems.
Original language | English |
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Title of host publication | 2021 IEEE International Symposium on Information Theory, ISIT 2021 - Proceedings |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 2625-2630 |
Number of pages | 6 |
ISBN (Electronic) | 9781538682098 |
DOIs | |
State | Published - 12 Jul 2021 |
Event | 2021 IEEE International Symposium on Information Theory, ISIT 2021 - Virtual, Melbourne, Australia Duration: 12 Jul 2021 → 20 Jul 2021 |
Publication series
Name | IEEE International Symposium on Information Theory - Proceedings |
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Volume | 2021-July |
ISSN (Print) | 2157-8095 |
Conference
Conference | 2021 IEEE International Symposium on Information Theory, ISIT 2021 |
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Country/Territory | Australia |
City | Virtual, Melbourne |
Period | 12/07/21 → 20/07/21 |
Bibliographical note
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