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Found 4 papers, 0 papers with code
Interaction Pursuit with Feature Screening and Selection
The suggested method first reduces the number of interactions and main effects to a moderate scale by a new feature screening approach, and then selects important interactions and main effects in the reduced feature space using regularization methods.
The constrained Dantzig selector with enhanced consistency
An important question is whether this factor can be reduced to a logarithmic factor of the sample size in ultra-high dimensions under mild regularity conditions.
Interaction pursuit in high-dimensional multi-response regression via distance correlation
Feature interactions can contribute to a large proportion of variation in many prediction models.
Innovated interaction screening for high-dimensional nonlinear classification
We propose a two-step procedure, IIS-SQDA, where in the first step an innovated interaction screening (IIS) approach based on transforming the original $p$-dimensional feature vector is proposed, and in the second step a sparse quadratic discriminant analysis (SQDA) is proposed for further selecting important interactions and main effects and simultaneously conducting classification.
