CoSeNet: A novel approach for optimal segmentation of correlation matrices-Reference-Cited by-同舟云学术

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CoSeNet: A novel approach for optimal segmentation of correlation matrices

Published:2024-01 Issue: Volume:144 Page:104270
ISSN:1051-2004
Container-title:Digital Signal Processing
language:en
Short-container-title:Digital Signal Processing

Author:

Palomo-Alonso A.,Casillas-Pérez D.,Jiménez-Fernández S.^ORCID,Portilla-Figueras A.,Salcedo-Sanz S.

Funder

Espana Ministerio de Ciencia e Innovacion

Ministerio de Ciencia e Innovación

Universidad de Alcalá

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Signal Processing,Artificial Intelligence,Applied Mathematics,Computer Vision and Pattern Recognition,Statistics, Probability and Uncertainty,Computational Theory and Mathematics

Reference64 articles.

1. Fully extracting feature correlation between and within stages for semantic segmentation;Yuan;Digit. Signal Process.,2022

2. On the application of cross correlation function to subsample discrete time delay estimation;Zhang;Digit. Signal Process.,2006

3. 2-D DOA estimation via correlation matrix reconstruction for nested l-shaped array;Yang;Digit. Signal Process.,2020

4. Sparse subspace clustering: algorithm, theory, and applications;Elhamifar;IEEE Trans. Pattern Anal. Mach. Intell.,2013

5. Structured sparse subspace clustering: a joint affinity learning and subspace clustering framework;Li;IEEE Trans. Image Process.,2017

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