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Universal and automatic elbow detection for learning the effective number of components in model selection problems

Morgado, Eduardo ; Martino, Luca ; Millán-Castillo, Roberto San

Digital signal processing, 2023-08, Vol.140, p.104103, Article 104103 [Periódico revisado por pares]

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Título:
Universal and automatic elbow detection for learning the effective number of components in model selection problems
Autor: Morgado, Eduardo ; Martino, Luca ; Millán-Castillo, Roberto San
Assuntos: Automatic elbow detection ; Clustering ; Model selection ; Order selection ; Variable selection
É parte de: Digital signal processing, 2023-08, Vol.140, p.104103, Article 104103
Descrição: We design a Universal Automatic Elbow Detector (UAED) for deciding the effective number of components in model selection problems. The relationship with the information criteria widely employed in the literature is also discussed. The proposed UAED does not require the knowledge of a likelihood function and can be easily applied in diverse applications, such as regression and classification, feature and/or order selection, clustering, and dimension reduction. Several experiments involving synthetic and real data show the advantages of the proposed scheme with benchmark techniques in the literature.
Editor: Elsevier Inc
Idioma: Inglês

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