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Joint Nuclear and $\ell_1$ Regularization for Logistic Matrix Regression with Applications to Brain ImagingJoint Nuclear and $\ell_1$ Regularization for Logistic Matrix Regression with Applications to Brain Imaging

Damian Brzyski · Aaron Cohen · Zijian Wang · Mario Dzemidzic · David A. Kareken 等 6 位作者首次提交 2026-06-12 · 修订 2026-06-12 · v1arXiv预印本

面向脑影像的 logistic 矩阵回归：核范数与 ℓ1 联合正则化AI 解读

We introduce a new convex optimization framework for logistic scalar-on-matrix regression which incorporates nuclear and $\ell_1$ norm penalties to enforce simultaneously low-rank and sparse structures in the estimated coefficient matrix. The proposed method enables interpretable modeling of high-dimensional matrix-valued predictors in the presence of binary responses. We derive a custom algorithm based on the Alternating Direction Method of Multipliers (ADMM) to efficiently…We introduce a new convex optimization framework for logistic scalar-on-matrix regression which incorporates nuclear and $\ell_1$ norm penalties to enforce simultaneously low-rank and sparse structures in the estimated coefficient matrix. The proposed method enables interpretable modeling of high-dimensional matrix-valued predictors in the presence of binary responses. We derive a custom algorithm based on the Alternating Direction Method of Multipliers (ADMM) to efficiently…

正则化与变量选择

关联记录被引 0 次 ↗OpenAlex · 核验 2026-10-12

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Joint Nuclear and ℓ1\ell_1ℓ1​ Regularization for Logistic Matrix Regression with Applications to Brain ImagingJoint Nuclear and $\ell_1$ Regularization for Logistic Matrix Regression with Applications to Brain Imaging

Joint Nuclear and $\ell_1$ Regularization for Logistic Matrix Regression with Applications to Brain ImagingJoint Nuclear and $\ell_1$ Regularization for Logistic Matrix Regression with Applications to Brain Imaging