Sparse Optimization for Transfer Learning: A L0-Regularized Framework for Multi-Source Domain Adaptation
This addresses the challenge of statistical bias and computational efficiency in multi-source domain adaptation for machine learning applications, but it appears incremental as it extends an existing paradigm with specific innovations.
This paper tackled the problem of transfer learning in heterogeneous multi-source environments with distributional divergence by proposing a Sparse Optimization for Transfer Learning (SOTL) framework based on L0-regularization, which significantly improved estimation accuracy and computational speed, especially under adversarial auxiliary domain conditions.
This paper explores transfer learning in heterogeneous multi-source environments with distributional divergence between target and auxiliary domains. To address challenges in statistical bias and computational efficiency, we propose a Sparse Optimization for Transfer Learning (SOTL) framework based on L0-regularization. The method extends the Joint Estimation Transferred from Strata (JETS) paradigm with two key innovations: (1) L0-constrained exact sparsity for parameter space compression and complexity reduction, and (2) refining optimization focus to emphasize target parameters over redundant ones. Simulations show that SOTL significantly improves both estimation accuracy and computational speed, especially under adversarial auxiliary domain conditions. Empirical validation on the Community and Crime benchmarks demonstrates the statistical robustness of the SOTL method in cross-domain transfer.