Domain adaptation methods for robust pattern recognition

Abstract

The large majority of classical and modern estimation techniques assume the data seen at the testing phase of statistical inference come from the same process that generated the training data. In many real-world applications this can be a restrictive assumption. We outline two solutions to overcome this heterogeneity: instance-weighting and dimension reduction. The instance-weighting methods estimate weights to use in a loss function in an attempt to make the weighted training distribution “look like” the testing distribution, whereas dimension reduction methods seek transformations of the training and testing data to place them both into a latent space where their distributions will be similar. We use synthetic datasets and a real data example to test the methods against one another.