Proximal Projection for Doubly Sparse Regularized Models
Regularization is often used in high-dimensional regression settings to generate a sparse model, which can save tremendous computing resources and identify predictors that are most strongly associated with the response. When the predictors can be represented by a Gaussian graphical model, the structure of the predictor graph can be exploited during regularization. Our proposed model exploits this underlying predictor graph structure by decomposing the estimated coefficient vector into a sum of latent variables that correspond to the sum of each node contribution to the coefficient vector. Regularization is then performed on the latent variables rather than on the coefficient vector direct...
| Authors | Jia Wei He, R. Ayesha Ali, Gerarda Darlington |
|---|---|
| Primary category | stat.ML |
| Published | 6 May 2026 |
| Last updated at source | 6 May 2026 |
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