  In this paper, a new approach for classification of target task using limited
labeled target data as well as enormous unlabeled source data is proposed which
is called self-taught learning. The target and source data can be drawn from
different distributions. In the previous approaches, covariate shift assumption
is considered where the marginal distributions p(x) change over domains and the
conditional distributions p(y|x) remain the same. In our approach, we propose a
new objective function which simultaneously learns a common space T(.) where
the conditional distributions over domains p(T(x)|y) remain the same and learns
robust SVM classifiers for target task using both source and target data in the
new representation. Hence, in the proposed objective function, the hidden label
of the source data is also incorporated. We applied the proposed approach on
Caltech-256, MSRC+LMO datasets and compared the performance of our algorithm to
the available competing methods. Our method has a superior performance to the
successful existing algorithms.
