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Found 4 papers, 0 papers with code
Parallel integrative learning for large-scale multi-response regression with incomplete outcomes
In this paper, we propose a scalable and computationally efficient procedure, called PEER, for large-scale multi-response regression with incomplete outcomes, where both the numbers of responses and predictors can be high-dimensional.
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.
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.
