{"ID":6138054,"CreatedAt":"2026-07-09T01:07:32.349475501Z","UpdatedAt":"2026-07-11T01:46:53.511787464Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.06936","arxiv_id":"2607.06936","title":"Transfer Learning for Linear Discriminant Analysis with a Shared Classification Signal","abstract":"This paper studies transfer learning for linear discriminant analysis in high-dimensional two-class classification. We consider one target domain and several source domains, where the mean difference in each domain is decomposed into a deterministic common component and a domain-specific random deviation. The common component represents a shared classification signal across domains, while the random deviation captures domain-specific heterogeneity. Under spiked covariance models, we derive deterministic limits for the target-domain Gaussian-calibrated error of weighted transfer classifiers under both homogeneous and heterogeneous covariance settings. These limits quantify the effects of the shared signal, domain-specific variation, dimension-to-sample-size ratios, and spike structures on transfer performance. They further lead to oracle transfer weights and consistent data-driven plug-in estimators. We also characterize the intercept bias induced by unbalanced target-domain class sample sizes and provide an asymptotically optimal correction.","short_abstract":"This paper studies transfer learning for linear discriminant analysis in high-dimensional two-class classification. We consider one target domain and several source domains, where the mean difference in each domain is decomposed into a deterministic common component and a domain-specific random deviation. The common co...","url_abs":"https://arxiv.org/abs/2607.06936","url_pdf":"https://arxiv.org/pdf/2607.06936v1","authors":"[\"Yonghan Zhang\",\"Yimeng Fan\",\"Wenya Luo\",\"Jiang Hu\"]","published":"2026-07-08T02:58:10Z","proceeding":"stat.ME","tasks":"[\"stat.ME\",\"stat.ML\"]","methods":"[]","has_code":false}