OWL-QN is based on the fact that when restricted to a single orthant, the L 1 regularizer is differentiable, and is in fact a linear function of θ.
our approach is based on building different Bayes classifiers as high level features and then learning a linear function of these classifiers for retrieval in the first task and classification in the second.