用稀疏优化提升多源迁移学习效果，兼顾精度与效率
Sparse Optimization for Transfer Learning: A L0-Regularized Framework for Multi-Source Domain Adaptation
- 基于L0正则化实现参数空间精确稀疏，压缩模型复杂度
- 在对抗性辅助域下仍保持高精度与快计算速度
- 适合处理异构多源数据的鲁棒迁移学习任务
本文研究异构多源环境下目标域与辅助域分布差异带来的迁移学习挑战。为应对统计偏差与计算效率问题，提出基于L0正则化的稀疏优化迁移学习框架（SOTL）。该方法在JETS范式基础上引入两项创新：(1) 通过L0约束实现参数空间的精确稀疏，降低模型复杂度；(2) 优化聚焦于目标参数，抑制冗余参数影响。仿真结果显示，SOTL显著提升估计精度与计算速度，尤其在对抗性辅助域条件下表现优异。在Community and Crime数据集上的实证验证表明，SOTL在跨域迁移中具备良好的统计鲁棒性。
原文摘要 · Abstract (English)
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.
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