PLFD - Portmanteau Local Feature Discrimination for Matrix-Variate Data
The portmanteau local feature discriminant approach first identifies the local discriminant features and their differential structures, then constructs the discriminant rule by pooling the identified local features together. This method is applicable to high-dimensional matrix-variate data. See the paper by Xu, Luo and Chen (2023, <doi:10.1007/s13171-021-00255-2>).
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3.70 score 4 scripts 416 downloadsdrrglm - Doubly Regularized Matrix-Variate Regression
The doubly regularized matrix-variate regression solves a low-rank-plus-sparse structure for matrix-variate generalized linear models through a weighted combination of nuclear-norm and L1-norm. The methodology implemented by this package is described in the paper "Doubly Regularized Matrix-Variate Regression". See Xu, Luo and Jiang <doi:10.1080/10618600.2026.2702534>.
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3.48 score 346 downloadsPPSFS - Partial Profile Score Feature Selection in High-Dimensional Generalized Linear Interaction Models
This is an implementation of the partial profile score feature selection (PPSFS) approach to generalized linear (interaction) models. The PPSFS is highly scalable even for ultra-high-dimensional feature space. See the paper by Xu, Luo and Chen (2022, <doi:10.4310/21-SII706>).
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openblascpp
2.70 score 1 stars 361 downloads