# Transfer Learning for Linear Discriminant Analysis with a Shared Classification Signal

arXiv: https://arxiv.org/abs/2607.06936 · HTML: https://mlsanity.com/paper/2607.06936/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 28 references that are in the map.
- First posted on arXiv 8 Jul 2026.

## Builds on (most cited first, 2 of 2)

- [Transfer learning via Regularized Linear Discriminant Analysis](https://mlsanity.com/paper/2501.02411/) (2025)
- [Structural Effect and Spectral Enhancement of High-Dimensional Regularized Linear Discriminant Analysis](https://mlsanity.com/paper/2507.16682/) (2025)

## Abstract (arXiv)

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.