Structured and sparse partial least squares coherence for multivariate cortico-muscular analysis

• URL: http://arxiv.org/abs/2503.21802v1
• Date: Tue, 25 Mar 2025 01:56:11 GMT
• Title: Structured and sparse partial least squares coherence for multivariate cortico-muscular analysis
• Authors: Jingyao Sun, Qilu Zhang, Di Ma, Tianyu Jia, Shijie Jia, Xiaoxue Zhai, Ruimou Xie, Ping-Ju Lin, Zhibin Li, Yu Pan, Linhong Ji, Chong Li,
• Abstract summary: We propose a structured and sparse partial least squares coherence algorithm (ssPLSC) to extract shared latent space representations related to cortico-muscular interactions.<n>We show that ssPLSC can achieve competitive or better performance over some representative cortico-muscular fusion methods.
• Score: 12.574818785692777
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multivariate cortico-muscular analysis has recently emerged as a promising approach for evaluating the corticospinal neural pathway. However, current multivariate approaches encounter challenges such as high dimensionality and limited sample sizes, thus restricting their further applications. In this paper, we propose a structured and sparse partial least squares coherence algorithm (ssPLSC) to extract shared latent space representations related to cortico-muscular interactions. Our approach leverages an embedded optimization framework by integrating a partial least squares (PLS)-based objective function, a sparsity constraint and a connectivity-based structured constraint, addressing the generalizability, interpretability and spatial structure. To solve the optimization problem, we develop an efficient alternating iterative algorithm within a unified framework and prove its convergence experimentally. Extensive experimental results from one synthetic and several real-world datasets have demonstrated that ssPLSC can achieve competitive or better performance over some representative multivariate cortico-muscular fusion methods, particularly in scenarios characterized by limited sample sizes and high noise levels. This study provides a novel multivariate fusion method for cortico-muscular analysis, offering a transformative tool for the evaluation of corticospinal pathway integrity in neurological disorders.

Related papers

• DisCo-DSO: Coupling Discrete and Continuous Optimization for Efficient Generative Design in Hybrid Spaces [12.729697787995892]
  DisCo-DSO is a novel approach that uses a generative model to learn a joint distribution over discrete and continuous design variables.<n>In particular, we illustrate DisCo-DSO's superiority over the state-of-the-art methods for interpretable reinforcement learning with decision trees.
  arXiv  Detail & Related papers  (2024-12-15T04:51:54Z)
• Kolmogorov-Smirnov GAN [52.36633001046723]
  We propose a novel deep generative model, the Kolmogorov-Smirnov Generative Adversarial Network (KSGAN) Unlike existing approaches, KSGAN formulates the learning process as a minimization of the Kolmogorov-Smirnov (KS) distance.
  arXiv  Detail & Related papers  (2024-06-28T14:30:14Z)
• Multiparameter regularization and aggregation in the context of polynomial functional regression [2.1960518939650475]
  We introduce an algorithm for multiple parameter regularization and present a theoretically grounded method for dealing with the associated parameters. The efficacy of the proposed approach is assessed through evaluations on both synthetic and some real-world medical data, revealing promising results.
  arXiv  Detail & Related papers  (2024-05-07T09:26:20Z)
• Ensemble Kalman Filtering Meets Gaussian Process SSM for Non-Mean-Field and Online Inference [47.460898983429374]
  We introduce an ensemble Kalman filter (EnKF) into the non-mean-field (NMF) variational inference framework to approximate the posterior distribution of the latent states. This novel marriage between EnKF and GPSSM not only eliminates the need for extensive parameterization in learning variational distributions, but also enables an interpretable, closed-form approximation of the evidence lower bound (ELBO) We demonstrate that the resulting EnKF-aided online algorithm embodies a principled objective function by ensuring data-fitting accuracy while incorporating model regularizations to mitigate overfitting.
  arXiv  Detail & Related papers  (2023-12-10T15:22:30Z)
• Learning Multiscale Consistency for Self-supervised Electron Microscopy Instance Segmentation [48.267001230607306]
  We propose a pretraining framework that enhances multiscale consistency in EM volumes. Our approach leverages a Siamese network architecture, integrating strong and weak data augmentations. It effectively captures voxel and feature consistency, showing promise for learning transferable representations for EM analysis.
  arXiv  Detail & Related papers  (2023-08-19T05:49:13Z)
• Faster Stochastic Variance Reduction Methods for Compositional MiniMax Optimization [50.10952609321302]
  compositional minimax optimization is a pivotal challenge across various machine learning domains. Current methods of compositional minimax optimization are plagued by sub-optimal complexities or heavy reliance on sizable batch sizes. This paper introduces a novel method, called Nested STOchastic Recursive Momentum (NSTORM), which can achieve the optimal sample complexity of $O(kappa3 /epsilon3 )$.
  arXiv  Detail & Related papers  (2023-08-18T14:57:21Z)
• Structured Optimal Variational Inference for Dynamic Latent Space Models [16.531262817315696]
  We consider a latent space model for dynamic networks, where our objective is to estimate the pairwise inner products plus the intercept of the latent positions. To balance posterior inference and computational scalability, we consider a structured mean-field variational inference framework.
  arXiv  Detail & Related papers  (2022-09-29T22:10:42Z)
• Revisiting GANs by Best-Response Constraint: Perspective, Methodology, and Application [49.66088514485446]
  Best-Response Constraint (BRC) is a general learning framework to explicitly formulate the potential dependency of the generator on the discriminator. We show that even with different motivations and formulations, a variety of existing GANs ALL can be uniformly improved by our flexible BRC methodology.
  arXiv  Detail & Related papers  (2022-05-20T12:42:41Z)
• Stochastic Gradient Descent-Ascent: Unified Theory and New Efficient Methods [73.35353358543507]
  Gradient Descent-Ascent (SGDA) is one of the most prominent algorithms for solving min-max optimization and variational inequalities problems (VIP) In this paper, we propose a unified convergence analysis that covers a large variety of descent-ascent methods. We develop several new variants of SGDA such as a new variance-reduced method (L-SVRGDA), new distributed methods with compression (QSGDA, DIANA-SGDA, VR-DIANA-SGDA), and a new method with coordinate randomization (SEGA-SGDA)
  arXiv  Detail & Related papers  (2022-02-15T09:17:39Z)
• Runtime Analysis of Single- and Multi-Objective Evolutionary Algorithms for Chance Constrained Optimization Problems with Normally Distributed Random Variables [11.310502327308575]
  We study the scenario of components that are independent and normally distributed. We introduce a multi-objective formulation of the problem which trades off the expected cost and its variance. We prove that this approach can also be used to compute a set of optimal solutions for the chance constrained minimum spanning tree problem.
  arXiv  Detail & Related papers  (2021-09-13T09:24:23Z)
• Harnessing Heterogeneity: Learning from Decomposed Feedback in Bayesian Modeling [68.69431580852535]
  We introduce a novel GP regression to incorporate the subgroup feedback. Our modified regression has provably lower variance -- and thus a more accurate posterior -- compared to previous approaches. We execute our algorithm on two disparate social problems.
  arXiv  Detail & Related papers  (2021-07-07T03:57:22Z)
• Jointly Modeling and Clustering Tensors in High Dimensions [6.072664839782975]
  We consider the problem of jointly benchmarking and clustering of tensors. We propose an efficient high-maximization algorithm that converges geometrically to a neighborhood that is within statistical precision.
  arXiv  Detail & Related papers  (2021-04-15T21:06:16Z)
• A Deep Learning approach to Reduced Order Modelling of Parameter Dependent Partial Differential Equations [0.2148535041822524]
  We develop a constructive approach based on Deep Neural Networks for the efficient approximation of the parameter-to-solution map. In particular, we consider parametrized advection-diffusion PDEs, and we test the methodology in the presence of strong transport fields.
  arXiv  Detail & Related papers  (2021-03-10T17:01:42Z)
• A Forward Backward Greedy approach for Sparse Multiscale Learning [0.0]
  We propose a feature driven Reproducing Kernel Hilbert space (RKHS) for which the associated kernel has a weighted multiscale structure. For generating approximations in this space, we provide a practical forward-backward algorithm that is shown to greedily construct a set of basis functions having a multiscale structure. We analyze the performance of the approach on a variety of simulation and real data sets.
  arXiv  Detail & Related papers  (2021-02-14T04:22:52Z)
• Hyperparameter Optimization in Neural Networks via Structured Sparse Recovery [54.60327265077322]
  We study two important problems in the automated design of neural networks through the lens of sparse recovery methods. In the first part of this paper, we establish a novel connection between HPO and structured sparse recovery. In the second part of this paper, we establish a connection between NAS and structured sparse recovery.
  arXiv  Detail & Related papers  (2020-07-07T00:57:09Z)

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.