CoSeNet: a novel approach for optimal segmentation of correlation matrices
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: inp...
| Autores: | , , , , |
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| Formato: | artículo |
| Fecha de publicación: | 2024 |
| País: | España |
| Recursos: | Universidad de Alcalá (UAH) |
| Repositorio: | e_Buah Biblioteca Digital Universidad de Alcalá |
| Idioma: | inglés |
| OAI Identifier: | oai:ebuah.uah.es:10017/60814 |
| Acesso em linha: | http://hdl.handle.net/10017/60814 https://dx.doi.org/10.1016/j.dsp.2023.104270 |
| Access Level: | acceso abierto |
| Palavra-chave: | Correlation matrices Segmentation algorithms Multi-algorithm architecture Metaheuristic optimization Machine learning Telecomunicaciones Telecommunications |
| Resumo: | 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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