  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.
