Multiparameter regularization and aggregation in the context of polynomial functional regression
This work addresses a specific bottleneck in functional regression for domains like medical data analysis, but it appears incremental as it extends existing regularization frameworks.
The authors tackled the limitation of single-parameter regularization in polynomial functional regression by introducing a multiparameter regularization algorithm with a method for parameter handling and model aggregation, showing promising results on synthetic and medical data.
Most of the recent results in polynomial functional regression have been focused on an in-depth exploration of single-parameter regularization schemes. In contrast, in this study we go beyond that framework by introducing an algorithm for multiple parameter regularization and presenting a theoretically grounded method for dealing with the associated parameters. This method facilitates the aggregation of models with varying regularization parameters. The efficacy of the proposed approach is assessed through evaluations on both synthetic and some real-world medical data, revealing promising results.