CoSeNet: A Novel Approach for Optimal Segmentation of Correlation Matrices

TL;DR

CoSeNet is a new neural network-based method designed to accurately identify correlated segments in noisy correlation matrices, outperforming previous approaches and adaptable to various applications.

Contribution

It introduces a four-layer architecture with an overlapping technique and heuristic optimization for parameter tuning, enhancing segmentation accuracy in noisy matrices.

Findings

Outperforms previous methods in segment detection accuracy.

Robust and generalizable due to use of pre-trained ML algorithms.

Produces noise-free binary matrices with segmentation points.

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 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.