Discriminative Noise Robust Sparse Orthogonal Label Regression-based Domain Adaptation
This method aims to improve domain adaptation performance for machine learning models by addressing data distribution mismatches and outlier sensitivity, which is a common challenge in real-world applications.
This paper proposes DOLL-DA, a novel unsupervised domain adaptation method that aligns source and target domain data discriminatively and statistically in a shared feature subspace. It also regresses data labels orthogonally using a label embedding trick and minimizes a Noise Robust Sparse Orthogonal Label Regression term to account for outliers and introduce sparsity.
Domain adaptation (DA) aims to enable a learning model trained from a source domain to generalize well on a target domain, despite the mismatch of data distributions between the two domains. State-of-the-art DA methods have so far focused on the search of a latent shared feature space where source and target domain data can be aligned either statistically and/or geometrically. In this paper, we propose a novel unsupervised DA method, namely Discriminative Noise Robust Sparse Orthogonal Label Regression-based Domain Adaptation (DOLL-DA). The proposed DOLL-DA derives from a novel integrated model which searches a shared feature subspace where source and target domain data are, through optimization of some repulse force terms, discriminatively aligned statistically, while at same time regresses orthogonally data labels thereof using a label embedding trick. Furthermore, in minimizing a novel Noise Robust Sparse Orthogonal Label Regression(NRS_OLR) term, the proposed model explicitly accounts for data outliers to avoid negative transfer and introduces the property of sparsity when regressing data labels. Due to the character restriction. Please read our detailed abstract in our paper.