4. The method as claimed in claim 1 , wherein said constructing comprises: determining, from said source domain, a set of bases for reconstructing said abbreviated text document examples; and, determining a set of classifiers for classifying said reconstructed abbreviated text document examples.
4. The method as claimed in claim 1 , wherein said constructing comprises: determining, from said source domain, a set of bases for reconstructing said abbreviated text document examples; and, determining a set of classifiers for classifying said reconstructed abbreviated text document examples. 5. The method as claimed in claim 4 , wherein said determining a set of bases comprises: formulating an optimization problem to generate an optimized set of activation coefficients for representing feature vectors of said constructed hidden feature space that the one or more classifiers are directly trained or used in predicting said labels; and, solving said problem to minimize a source domain text document reconstruction error while simultaneously minimizing an error in the learned classification model using said one or more tagged labels.