Discriminative Noise Robust Sparse Orthogonal Label Regression-based Domain Adaptation

TL;DR

This paper introduces DOLL-DA, a novel unsupervised domain adaptation method that aligns source and target data in a shared feature space while explicitly handling outliers and promoting discriminative, noise-robust label regression.

Contribution

The paper proposes a new unsupervised domain adaptation approach combining discriminative alignment, noise robustness, and sparse orthogonal label regression, addressing outliers and negative transfer.

Findings

Effective in aligning source and target domains

Handles data outliers explicitly

Improves adaptation performance

Abstract

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.