CoSeNet: A Novel Approach for Optimal Segmentation of Correlation Matrices
This work addresses the challenge of optimal segmentation in correlation matrices for applications requiring efficiency, memory, and speed trade-offs, representing an incremental improvement over existing methods.
The paper tackles the problem of identifying correlated segments in noisy correlation matrices, proposing CoSeNet, which achieves better performance than previous approaches by using a four-layer algorithmic architecture with overlapping techniques and pre-trained ML algorithms.
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.