Title: Domain Generalization by Marginal Transfer Learning,
Abstract: Domain generalization is the problem of assigning class labels to an
unlabeled test data set, given several labeled training data sets drawn from
similar distributions. This problem arises in several applications where data
distributions fluctuate because of biological, technical, or other sources of
variation. We develop a distribution-free, kernel-based approach that predicts
a classifier from the marginal distribution of features, by leveraging the
trends present in related classification tasks. This approach involves
identifying an appropriate reproducing kernel Hilbert space and optimizing a
regularized empirical risk over the space. We present generalization error
analysis, describe universal kernels, and establish universal consistency of
the proposed methodology. Experimental results on synthetic data and three real
data applications demonstrate the superiority of the method with respect to a
pooling strategy.