Structured Radial Basis Function Network: Modelling Diversity for Multiple Hypotheses Prediction
This work addresses multi-modal regression problems for applications like air quality and energy prediction, but it is incremental as it builds on existing ensemble and multiple hypothesis methods with a novel parametric control mechanism.
The paper tackles the challenge of integrating multiple hypothesis frameworks into learning models for multi-modal regression problems by introducing a Structured Radial Basis Function Network (s-RBFN) that uses centroidal Voronoi tessellations and a radial basis function network to control diversity. Empirical validation on air quality and energy appliance datasets demonstrates superior generalization performance and computational efficiency compared to other models.
Multi-modal problems can be effectively addressed using multiple hypothesis frameworks, but integrating these frameworks into learning models poses significant challenges. This paper introduces a Structured Radial Basis Function Network (s-RBFN) as an ensemble of multiple hypothesis predictors for regression. During the training of the predictors, first the centroidal Voronoi tessellations are formed based on their losses and the true labels, representing geometrically the set of multiple hypotheses. Then, the trained predictors are used to compute a structured dataset with their predictions, including centers and scales for the basis functions. A radial basis function network, with each basis function focused on a particular hypothesis, is subsequently trained using this structured dataset for multiple hypotheses prediction. The s-RBFN is designed to train efficiently while controlling diversity in ensemble learning parametrically. The least-squares approach for training the structured ensemble model provides a closed-form solution for multiple hypotheses and structured predictions. During the formation of the structured dataset, a parameter is employed to avoid mode collapse by controlling tessellation shapes. This parameter provides a mechanism to balance diversity and generalization performance for the s-RBFN. The empirical validation on two multivariate prediction datasets-air quality and energy appliance predictions-demonstrates the superior generalization performance and computational efficiency of the structured ensemble model compared to other models and their single-hypothesis counterparts.