Domain Generalization via Invariant Feature Representation

Abstract

This paper investigates domain generalization: How to take knowledge acquired from an arbitrary number of related domains and apply it to previously unseen domains? We propose Domain-Invariant Component Analysis (DICA), a kernel-based optimization algorithm that learns an invariant transformation by minimizing the dissimilarity across domains, whilst preserving the functional relationship between input and output variables. A learning-theoretic analysis shows that reducing dissimilarity improves the expected generalization ability of classifiers on new domains, motivating the proposed algorithm. Experimental results on synthetic and real-world datasets demonstrate that DICA successfully learns invariant features and improves classifier performance in practice.

The 30th International Conference on Machine Learning (ICML 2013)

References in corpus (2)

• Learning from Distributions via Support Measure Machines
• Flexible Modeling of Latent Task Structures in Multitask Learning