# Joint cross-domain classification and subspace learning for unsupervised adaptation

arXiv: https://arxiv.org/abs/1411.4491 · HTML: https://mlsanity.com/paper/1411.4491/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Nov 2014.

## Built on by (most cited first, 1 of 1)

- [Weakly supervised covariance matrices alignment through Stiefel matrices estimation for MEG applications](https://mlsanity.com/paper/2402.03345/) (2024)

## Abstract (arXiv)

Domain adaptation aims at adapting the knowledge acquired on a source domain to a new different but related target domain. Several approaches have beenproposed for classification tasks in the unsupervised scenario, where no labeled target data are available. Most of the attention has been dedicated to searching a new domain-invariant representation, leaving the definition of the prediction function to a second stage. Here we propose to learn both jointly. Specifically we learn the source subspace that best matches the target subspace while at the same time minimizing a regularized misclassification loss. We provide an alternating optimization technique based on stochastic sub-gradient descent to solve the learning problem and we demonstrate its performance on several domain adaptation tasks.