Error analysis for coefficient-based regularized regression in additive models

This paper considers a coefficient-based additive model with the ℓq-regularizer (1≤q≤2). Error bounds are established for the proposed model by integrating the stepping stone technique and the concentration estimate with empirical covering numbers. From error analysis, we obtain a sharp learning rate that can be arbitrarily close to O(nϵ−1) under mild conditions.