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Spectral information criterion for automatic elbow detection

dc.contributor.authorMartino, Luca
dc.contributor.authorSan Millán-Castillo, Roberto
dc.contributor.authorMorgado, Eduardo
dc.date.accessioned2023-10-13T07:47:54Z
dc.date.available2023-10-13T07:47:54Z
dc.date.issued2023
dc.identifier.citationLuca Martino, Roberto San Millán-Castillo, Eduardo Morgado, Spectral information criterion for automatic elbow detection, Expert Systems with Applications, Volume 231, 2023, 120705, ISSN 0957-4174, https://doi.org/10.1016/j.eswa.2023.120705es
dc.identifier.issn0957-4174
dc.identifier.urihttps://hdl.handle.net/10115/24856
dc.descriptionThe work was partially supported by the Young Researchers R&D Project, ref. num. F861 (AUTO-BA-GRAPH) funded by Community of Madrid and Rey Juan Carlos University, Spain, and by Agencia Estatal de Investigación AEI, Spain (project SP-GRAPH, ref. num. PID2019-105032GB-I00).es
dc.description.abstractWe introduce a generalized information criterion that contains other well-known information criteria, such as Bayesian information Criterion (BIC) and Akaike information criterion (AIC), as special cases. Furthermore, the proposed spectral information criterion (SIC) is also more general than the other information criteria, e.g., since the knowledge of a likelihood function is not strictly required. SIC extracts geometric features of the error curve and, as a consequence, it can be considered an automatic elbow detector. SIC provides a subset of all possible models, with a cardinality that often is much smaller than the total number of possible models. The elements of this subset are ‘‘elbows’’ of the error curve. A practical rule for selecting a unique model within the sets of elbows is suggested as well. Theoretical invariance properties of SIC are analyzed. Moreover, we test SIC in ideal scenarios where provides always the optimal expected results. We also test SIC in several numerical experiments: some involving synthetic data, and two experiments involving real datasets. They are all real-world applications such as clustering, variable selection, or polynomial order selection, to name a few. The results show the benefits of the proposed scheme. Matlab code related to the experiments is also provided. Possible future research lines are finally discussed.es
dc.language.isoenges
dc.publisherElsevieres
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectModel selectiones
dc.subjectAutomatic elbow detectiones
dc.subjectInformation criteriones
dc.subjectBICes
dc.subjectAICes
dc.subjectMarginal likelihoodes
dc.titleSpectral information criterion for automatic elbow detectiones
dc.typeinfo:eu-repo/semantics/articlees
dc.identifier.doi10.1016/j.eswa.2023.120705es
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses


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Attribution-NonCommercial-NoDerivatives 4.0 InternacionalExcept where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 Internacional