Work

Domain Generalization via Multidomain Discriminant Analysis

Year: 2019

Type: conference-paper

Abstract: Domain generalization (DG) aims to incorporate knowledge from multiple source domains into a single model that could generalize well on unseen target domains. This problem is ubiquitous in practice since the distributions of the target data may rarely be identical to those of the source data. In this paper, we propose Multidomain Discriminant Analysis (MDA) to address DG of classification tasks in general situations. MDA learns a domain-invariant feature transformation that aims to achieve appealing properties, including a minimal divergence among domains within each class, a maximal separability among classes, and overall maximal compactness of all classes. Furthermore, we provide the bounds on excess risk and generalization error by learning theory analysis. Comprehensive experiments on synthetic and real benchmark datasets demonstrate the effectiveness of MDA.

Source: PubMed

DOI: 10.48550/arxiv.1907.11216

Authors: Hu, Shoubo, Zhang, Kun, Chen, Zhitang, Chan, Laiwan

Language: English

Topic: Domain Adaptation and Few-Shot Learning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: National Institute of Biomedical Imaging and Bioengineering, U.S. National Library of Medicine

Awards: INTEGRATIVE GRAPHICAL MODELS FOR LARGE MULTI-MODAL BIOMEDICAL DATA, EFFECTIVE CONNECTIVITY IN BRAIN NETWORKS: Discovering Latent Structure, Network Complexity and Recurrence.

OpenAlex ID: W2963804252

FWCI: 3.059

Cites: 35

Cited by: 72

Related to: 10