CoSeNet: a novel approach for optimal segmentation of correlation matrices
Authors
Palomo Alonso, Alberto; Casillas Pérez, David; Jiménez Fernández, Silvia; Portilla Figueras, José Antonio; Salcedo Sanz, SanchoIdentifiers
Permanent link (URI): http://hdl.handle.net/10017/60814DOI: 10.1016/j.dsp.2023.104270
ISSN: 1051-2004
Publisher
Elsevier
Date
2024-01-01Affiliation
Universidad de Alcalá. Departamento de Electrónica; Universidad de Alcalá. Departamento de Teoría de la Señal y ComunicacionesFunders
Agencia Estatal de Investigación
Bibliographic citation
Palomo Alonso, A., Casillas Pérez, D., Jiménez Fernández, S., Portilla Figueras, J.A. & Salcedo Sanz, S. 2024, "CoSeNet: a novel approach for optimal segmentation of correlation matrices", Digital Signal Processing, vol. 144, art. no. 104270, pp. 1-16.
Keywords
Correlation matrices
Segmentation algorithms
Multi-algorithm architecture
Metaheuristic optimization
Machine learning
Project
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-115454GB-C21/ES/NUEVOS ALGORITMOS NEURO-EVOLUTIVOS PARA CLASIFICACION ORDINAL: APLICACIONES EN CLIMA, ENERGIAS LIMPIAS Y MEDIO AMBIENTE/
Document type
info:eu-repo/semantics/article
Version
info:eu-repo/semantics/publishedVersion
Publisher's version
https://doi.org/10.1016/j.dsp.2023.104270Rights
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
© 2023 The authors
Access rights
info:eu-repo/semantics/openAccess
Abstract
In this paper, we propose a novel approach for the optimal identification of correlated segments in noisy correlation matrices. The proposed model is known as CoSeNet (Correlation Segmentation Network) and is based on a four-layer algorithmic architecture that includes several processing layers: input, formatting, re-scaling, and segmentation layer. The proposed model can effectively identify correlated segments in such matrices, better than previous approaches for similar problems. Internally, the proposed model utilizes an overlapping technique and uses pre-trained Machine Learning (ML) algorithms, which makes it robust and generalizable. CoSeNet approach also includes a method that optimizes the parameters of the re-scaling layer using a heuristic algorithm and fitness based on a Window Difference-based metric. The output of the model is a binary noise-free matrix representing optimal segmentation as well as its segmentation points and can be used in a variety of applications, obtaining compromise solutions between efficiency, memory, and speed of the proposed deployment model.
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