2014/09/18 by Basura Fernando, Amaury Habrard, Fernando, Basura +5 · 4 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · Medicine · #Cancer-related molecular mechanisms research #Computer Vision and Pattern Recognition (cs.CV) #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Respiratory viral infections research #cs.CV
paper · pdf · doi:10.48550/arxiv.1409.5241
openalex publication_date 2014/09/18 · arxiv created 2014/10/23 · arxiv updated 2014/10/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this paper, we introduce a new domain adaptation (DA) algorithm where the source and target domains are represented by subspaces spanned by eigenvectors. Our method seeks a domain invariant feature space by learning a mapping function which aligns the source subspace with the target one. We show that the solution of the corresponding optimization problem can be obtained in a simple closed form, leading to an extremely fast algorithm. We present two approaches to determine the only hyper-parameter in our method corresponding to the size of the subspaces. In the first approach we tune the size of subspaces using a theoretical bound on the stability of the obtained result. In the second approach, we use maximum likelihood estimation to determine the subspace size, which is particularly useful for high dimensional data. Apart from PCA, we propose a subspace creation method that outperform partial least squares (PLS) and linear discriminant analysis (LDA) in domain adaptation. We test our method on various datasets and show that, despite its intrinsic simplicity, it outperforms state of the art DA methods.