A Muon-Accelerated Algorithm for Low Separation Rank Tensor Generalized Linear Models
Tensor-valued data arise naturally in multidimensional signal and imaging problems, such as biomedical imaging. When incorporated into generalized linear models (GLMs), naive vectorization can destroy their multi-way structure and lead to high-dimensional, ill-posed estimation. To address this challenge, Low Separation Rank (LSR) decompositions reduce model complexity by imposing low-rank multilinear structure on the coefficient tensor. A representative approach for estimating LSR-based tensor GLMs (LSR-TGLMs) is the Low Separation Rank Tensor Regression (LSRTR) algorithm, which adopts block coordinate descent and enforces orthogonality of the factor matrices through repeated QR-based pro...
| Authors | Xiao Liang, Shuang Li |
|---|---|
| Primary category | stat.ML |
| Published | 6 April 2026 |
| Last updated at source | 6 April 2026 |
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