Pareto-Optimal Clustering with the Primal Deterministic Information Bottleneck

At the heart of both lossy compression and clustering is a trade-off between the fidelity and size of the learned representation. Our goal is to map out and study the Pareto frontier that quantifies this trade-off. We focus on the optimization of the Deterministic Information Bottleneck (DIB) object...

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Detalles Bibliográficos
Autores principales: Tan, Andrew K., Tegmark, Max, Chuang, Isaac L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9222302/
https://www.ncbi.nlm.nih.gov/pubmed/35741492
http://dx.doi.org/10.3390/e24060771

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9222302/
https://www.ncbi.nlm.nih.gov/pubmed/35741492
http://dx.doi.org/10.3390/e24060771

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