15. A system for processing documents comprising: a memory; a processor unit in communication with the memory that performs a method to: construct a hidden feature space from one or more text document examples of a source domain, said one or more text document examples tagged with one or more labels of a source domain label space; train one or more classifiers for use in classifying text documents, said classifiers trained on said one or more text document examples and said hidden feature space; receive, at said processor device, one or more abbreviated text document examples from a target domain; and learn a classification model from said hidden feature space, said hidden feature space constructing comprising: reconstructing, from said source domain, said abbreviated text document examples; and, determining a set of classifiers for classifying said reconstructed abbreviated text document examples, said reconstructing comprising: 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 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; and, predicting the labels of said abbreviated text document examples based on the reconstructed target domain text document examples and said one or more trained classifiers.
15. A system for processing documents comprising: a memory; a processor unit in communication with the memory that performs a method to: construct a hidden feature space from one or more text document examples of a source domain, said one or more text document examples tagged with one or more labels of a source domain label space; train one or more classifiers for use in classifying text documents, said classifiers trained on said one or more text document examples and said hidden feature space; receive, at said processor device, one or more abbreviated text document examples from a target domain; and learn a classification model from said hidden feature space, said hidden feature space constructing comprising: reconstructing, from said source domain, said abbreviated text document examples; and, determining a set of classifiers for classifying said reconstructed abbreviated text document examples, said reconstructing comprising: 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 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; and, predicting the labels of said abbreviated text document examples based on the reconstructed target domain text document examples and said one or more trained classifiers. 21. The system as claimed in claim 15 , wherein said source domain includes multi-labeled text document examples, and said abbreviated target domain text documents include one or more of: a web page of limited character length, a mobile text message of limited character length, an instant message of limited character length, an online blog of limited character length, a tweet, a weblink, a paper abstract, and, a newsfeed.