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Universality of Logarithmic Loss in Fixed-Length Lossy Compression †

We established a universality of logarithmic loss over a finite alphabet as a distortion criterion in fixed-length lossy compression. For any fixed-length lossy-compression problem under an arbitrary distortion criterion, we show that there is an equivalent lossy-compression problem under logarithmi...

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Detalles Bibliográficos
Autor principal: No, Albert
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7515068/
https://www.ncbi.nlm.nih.gov/pubmed/33267294
http://dx.doi.org/10.3390/e21060580
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author No, Albert
author_facet No, Albert
author_sort No, Albert
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description We established a universality of logarithmic loss over a finite alphabet as a distortion criterion in fixed-length lossy compression. For any fixed-length lossy-compression problem under an arbitrary distortion criterion, we show that there is an equivalent lossy-compression problem under logarithmic loss. The equivalence is in the strong sense that we show that finding good schemes in corresponding lossy compression under logarithmic loss is essentially equivalent to finding good schemes in the original problem. This equivalence relation also provides an algebraic structure in the reconstruction alphabet, which allows us to use known techniques in the clustering literature. Furthermore, our result naturally suggests a new clustering algorithm in the categorical data-clustering problem.
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spelling pubmed-75150682020-11-09 Universality of Logarithmic Loss in Fixed-Length Lossy Compression † No, Albert Entropy (Basel) Article We established a universality of logarithmic loss over a finite alphabet as a distortion criterion in fixed-length lossy compression. For any fixed-length lossy-compression problem under an arbitrary distortion criterion, we show that there is an equivalent lossy-compression problem under logarithmic loss. The equivalence is in the strong sense that we show that finding good schemes in corresponding lossy compression under logarithmic loss is essentially equivalent to finding good schemes in the original problem. This equivalence relation also provides an algebraic structure in the reconstruction alphabet, which allows us to use known techniques in the clustering literature. Furthermore, our result naturally suggests a new clustering algorithm in the categorical data-clustering problem. MDPI 2019-06-10 /pmc/articles/PMC7515068/ /pubmed/33267294 http://dx.doi.org/10.3390/e21060580 Text en © 2019 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
No, Albert
Universality of Logarithmic Loss in Fixed-Length Lossy Compression †
title Universality of Logarithmic Loss in Fixed-Length Lossy Compression †
title_full Universality of Logarithmic Loss in Fixed-Length Lossy Compression †
title_fullStr Universality of Logarithmic Loss in Fixed-Length Lossy Compression †
title_full_unstemmed Universality of Logarithmic Loss in Fixed-Length Lossy Compression †
title_short Universality of Logarithmic Loss in Fixed-Length Lossy Compression †
title_sort universality of logarithmic loss in fixed-length lossy compression †
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7515068/
https://www.ncbi.nlm.nih.gov/pubmed/33267294
http://dx.doi.org/10.3390/e21060580
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