FASTER: Feature Alignment and Structured Transfer via Efficient Regularization

Music City Center 

Most existing transfer learning methods assume identical feature spaces for all domains. However, differences in data collection often create feature variations across domains, making the feature space heterogeneous. To address this, we propose FASTER (Feature Alignment and Structured Transfer via Efficient Regularization), a novel two-step transfer learning framework that integrates regularized feature alignment with structured modeling to enhance knowledge transfer. FASTER first aligns source and target domains by learning structured feature mappings through covariance-regularized optimization, ensuring effective information transfer despite feature differences. In the second step, a joint predictive model is trained on the mapped source and target data by minimizing a regularized loss function, followed by an adaptive correction term that refines task-specific differences. Our approach reduces domain disparity while preserving interpretability through structured regularization. Extensive simulations and real-data experiments validate the effectiveness of FASTER in heterogeneous feature adaptation, providing a principled solution for transfer learning across diverse domains.

Transfer Learning

Heterogeneous Feature Space

Feature Alignment

Regularization 

Section on Statistical Learning and Data Science