提出CoSeNet模型，高效识别噪声相关矩阵中的关联段。
CoSeNet: A Novel Approach for Optimal Segmentation of Correlation Matrices
- 四层架构结合重叠技术与预训练算法，提升分割鲁棒性。
- 通过基于窗口差的适应度优化重缩放层参数，实现更优分割结果。
- 输出二值化无噪矩阵，适合对效率、内存、速度有平衡需求的应用。
本文提出一种新型方法CoSeNet（相关性分割网络），用于在噪声相关矩阵中最优识别关联段。该模型采用四层架构，包括输入、格式化、重缩放和分割层。内部使用重叠技术及预训练机器学习算法，增强模型鲁棒性与泛化能力。同时，通过基于窗口差的启发式算法优化重缩放层参数，提升分割精度。模型输出为二值化无噪矩阵，包含最优分割点，可应用于多种场景，在效率、内存占用和处理速度之间实现良好权衡。
原文摘要 · Abstract (English)
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 Seg-mentation 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 seg-mentation 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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