CoSeNet: A Novel Approach for Optimal Segmentation of Correlation Matrices

• URL: http://arxiv.org/abs/2512.21000v1
• Date: Wed, 24 Dec 2025 06:55:43 GMT
• Title: CoSeNet: A Novel Approach for Optimal Segmentation of Correlation Matrices
• Abstract summary: The proposed model is known as CoSeNet and is based on a four-layer algorithmic architecture.<n>The model can effectively identify correlated segments in such matrices, better than previous approaches for similar problems.<n>The output of the model is a binary noise-free matrix representing optimal segmentation as well as its seg-mentation points.
• Score: 3.177209535397463
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

Related papers

• Joint Representation Learning and Clustering via Gradient-Based Manifold Optimization [1.7484661165784237]
  We propose a Manifold Learning Framework that learns dimensionality reduction and clustering simultaneously.<n>The proposed framework is able to jointly learn the parameters of a dimension reduction technique and cluster the data.<n>Our algorithm has better performance than popular clustering algorithms from the literature.
  arXiv  Detail & Related papers  (2026-04-15T05:18:27Z)
• Generalized Optimal Classification Trees: A Mixed-Integer Programming Approach [17.725629133949955]
  Mixed-integer programming (MIP) offers a high degree of modeling flexibility.<n>We propose a MIP-based framework for learning optimal classification trees under nonlinear performance metrics.<n>We evaluate the proposed approach on 50 benchmark datasets.
  arXiv  Detail & Related papers  (2026-02-02T14:46:01Z)
• Simulation-Based Fitting of Intractable Models via Sequential Sampling and Local Smoothing [0.0]
  The algorithm combines a global search phase, aimed at identifying the region of the solution, with a local search phase that mimics a trust region version of the Fisher scoring algorithm for computing a quasi-likelihood estimator.<n>An R package implementing the algorithm is available on CRAN.
  arXiv  Detail & Related papers  (2025-11-11T12:43:18Z)
• Ensemble Quadratic Assignment Network for Graph Matching [52.20001802006391]
  Graph matching is a commonly used technique in computer vision and pattern recognition. Recent data-driven approaches have improved the graph matching accuracy remarkably. We propose a graph neural network (GNN) based approach to combine the advantages of data-driven and traditional methods.
  arXiv  Detail & Related papers  (2024-03-11T06:34:05Z)
• An Efficient Algorithm for Clustered Multi-Task Compressive Sensing [60.70532293880842]
  Clustered multi-task compressive sensing is a hierarchical model that solves multiple compressive sensing tasks. The existing inference algorithm for this model is computationally expensive and does not scale well in high dimensions. We propose a new algorithm that substantially accelerates model inference by avoiding the need to explicitly compute these covariance matrices.
  arXiv  Detail & Related papers  (2023-09-30T15:57:14Z)
• Accelerating Cutting-Plane Algorithms via Reinforcement Learning Surrogates [49.84541884653309]
  A current standard approach to solving convex discrete optimization problems is the use of cutting-plane algorithms. Despite the existence of a number of general-purpose cut-generating algorithms, large-scale discrete optimization problems continue to suffer from intractability. We propose a method for accelerating cutting-plane algorithms via reinforcement learning.
  arXiv  Detail & Related papers  (2023-07-17T20:11:56Z)
• Exploiting Temporal Structures of Cyclostationary Signals for Data-Driven Single-Channel Source Separation [98.95383921866096]
  We study the problem of single-channel source separation (SCSS) We focus on cyclostationary signals, which are particularly suitable in a variety of application domains. We propose a deep learning approach using a U-Net architecture, which is competitive with the minimum MSE estimator.
  arXiv  Detail & Related papers  (2022-08-22T14:04:56Z)
• Optimal Variable Clustering for High-Dimensional Matrix Valued Data [3.1138411427556445]
  We propose a new latent variable model for the features arranged in matrix form. Under mild conditions, our algorithm attains clustering consistency in the high-dimensional setting. We identify the optimal weight in the sense that using this weight guarantees our algorithm to be minimax rate-optimal.
  arXiv  Detail & Related papers  (2021-12-24T02:13:04Z)
• An iterative clustering algorithm for the Contextual Stochastic Block Model with optimality guarantees [4.007017852999008]
  We propose a new iterative algorithm to cluster networks with side information for nodes. We show that our algorithm is optimal under the Contextual Symmetric Block Model.
  arXiv  Detail & Related papers  (2021-12-20T12:04:07Z)
• Unfolding Projection-free SDP Relaxation of Binary Graph Classifier via GDPA Linearization [59.87663954467815]
  Algorithm unfolding creates an interpretable and parsimonious neural network architecture by implementing each iteration of a model-based algorithm as a neural layer. In this paper, leveraging a recent linear algebraic theorem called Gershgorin disc perfect alignment (GDPA), we unroll a projection-free algorithm for semi-definite programming relaxation (SDR) of a binary graph. Experimental results show that our unrolled network outperformed pure model-based graph classifiers, and achieved comparable performance to pure data-driven networks but using far fewer parameters.
  arXiv  Detail & Related papers  (2021-09-10T07:01:15Z)
• Kernel Clustering with Sigmoid-based Regularization for Efficient Segmentation of Sequential Data [3.8326963933937885]
  segmentation aims at partitioning a data sequence into several non-overlapping segments that may have nonlinear and complex structures. A popular Kernel for optimally solving this problem is dynamic programming (DP), which has quadratic computation and memory requirements. Although many algorithms have been proposed to approximate the optimal segmentation, they have no guarantee on the quality of their solutions.
  arXiv  Detail & Related papers  (2021-06-22T04:32:21Z)
• Multi-View Spectral Clustering with High-Order Optimal Neighborhood Laplacian Matrix [57.11971786407279]
  Multi-view spectral clustering can effectively reveal the intrinsic cluster structure among data. This paper proposes a multi-view spectral clustering algorithm that learns a high-order optimal neighborhood Laplacian matrix. Our proposed algorithm generates the optimal Laplacian matrix by searching the neighborhood of the linear combination of both the first-order and high-order base.
  arXiv  Detail & Related papers  (2020-08-31T12:28:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.