A Muon-Accelerated Algorithm for Low Separation Rank Tensor Generalized Linear Models

Xiao Liang and Shuang Li have proposed a cutting-edge algorithm, LSRTR-M, that enhances the estimation process for Low Separation Rank Tensor Generalized Linear Models (TGLMs). By integrating Muon updates into the established Low Separation Rank Tensor Regression (LSRTR) framework, LSRTR-M retains the original block coordinate descent strategy while optimizing the update process. This innovative approach leads to significantly faster convergence, both in terms of iteration count and overall processing time. The algorithm shows promise in reducing normalized estimation and prediction errors across various synthetic tasks including linear, logistic, and Poisson models.

In practical applications, LSRTR-M has demonstrated improved performance on the Vessel MNIST 3D task, showcasing its potential for efficient classification while maintaining competitive results. The introduction of this Muon-accelerated algorithm marks a pivotal advancement in handling tensor-valued data, commonly found in multidimensional signal and imaging problems such as biomedical imaging. The ability to efficiently estimate low-rank multilinear structures not only enhances model performance but also opens new possibilities for complex data analysis in machine learning and signal processing domains.