Domain Adaptation with Coupled Subspaces
Venue
Artificial Intelligence and Statistics (2011)
Publication Year
2011
Authors
John Blitzer, Sham Kakade, Dean Foster
BibTeX
Abstract
Domain adaptation algorithms address a key issue in applied machine learning: How
can we train a system under a source distribution but achieve high performance
under a different target distribution? We tackle this question for divergent
distributions where crucial predictive target features may not even have support
under the source distribution. In this setting, the key intuition is that that if
we can link target-specific features to source features, we can learn effectively
using only source labeled data. We formalize this intuition, as well as the
assumptions under which such coupled learning is possible. This allows us to give
finite sample target error bounds (using only source training data) and an algorithm
which performs at the state-of-the-art on two natural language processing
adaptation tasks which are characterized by novel target features.
