Intrinsic dimension estimation for discrete metrics
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- dc.contributor.author Macocco, Iuri
- dc.contributor.author Glielmo, Aldo
- dc.contributor.author Grilli, Jacopo
- dc.contributor.author Laio, Alessandro
- dc.date.accessioned 2025-05-06T06:12:21Z
- dc.date.available 2025-05-06T06:12:21Z
- dc.date.issued 2023
- dc.description.abstract Real-world datasets characterized by discrete features are ubiquitous: from categorical surveys to clinical questionnaires, from unweighted networks to DNA sequences. Nevertheless, the most common unsupervised dimensional reduction methods are designed for continuous spaces, and their use for discrete spaces can lead to errors and biases. In this Letter we introduce an algorithm to infer the intrinsic dimension (ID) of datasets embedded in discrete spaces. We demonstrate its accuracy on benchmark datasets, and we apply it to analyze a metagenomic dataset for species fingerprinting, finding a surprisingly small ID, of order 2. This suggests that evolutive pressure acts on a low-dimensional manifold despite the high dimensionality of sequences’ space.en
- dc.description.sponsorship The authors thank Antonietta Mira, Alex Rodriguez and Marcello Dalmonte for the fruitful discussions. AG and AL acknowledge support from the European Union’s Horizon 2020 research and innovation program (Grant No. 824143, MaX ‘Materials design at the eXascale’ Centre of Excellence).en
- dc.format.mimetype application/pdf
- dc.identifier.citation Macocco I, Glielmo A, Grilli J, Laio A. Intrinsic dimension estimation for discrete metrics. Phys Rev Lett. 2023 Feb 8;130(6):067401. DOI: 10.1103/PhysRevLett.130.067401
- dc.identifier.doi http://dx.doi.org/10.1103/PhysRevLett.130.067401
- dc.identifier.issn 0031-9007
- dc.identifier.uri http://hdl.handle.net/10230/70298
- dc.language.iso eng
- dc.publisher American Physical Society
- dc.relation.ispartof Physical Review Letters. 2023 Feb 8;130(6):067401
- dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/824143
- dc.rights © American Physical Society. Published article available at https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.130.067401
- dc.rights.accessRights info:eu-repo/semantics/openAccess
- dc.subject.other ADN -- Síntesica
- dc.subject.other Algorismesca
- dc.subject.other Genòmicaca
- dc.title Intrinsic dimension estimation for discrete metricsen
- dc.type info:eu-repo/semantics/article
- dc.type.version info:eu-repo/semantics/publishedVersion