Sparse feature space representation: A unified framework for semi-supervised and domain adaptation learning

In a semi-supervised domain adaptation (DA) task, one has access to only few labeled target examples. In this case, the success of DA needs the effective utilization of a large number of unlabeled target data to extract more discriminative information that is useful for generalization. To this end, we exploit in this paper the feature space embeddings of the target data as well as multi-source prior models to augment the discrimination space for the target function learning. Therefore, we propose a novel multi-source adaptation learning framework based on Sparse Feature Space Representation (SFSR), or called SFSR-MSAL for short. Specifically, the SFSR algorithm is first presented for the further construction of robust graph, on which the discriminative information can be smoothly propagated into the unlabeled target data by additionally incorporating the geometric structure of the target data. Considering the robustness in the semi-supervised DA, we replace the traditional l2-norm based least squares regression with the l2, 1-norm sparse regression, and then construct the SFSR-graph based semi-supervised DA framework with multi-source adaptation constraints. Our framework is universal and can be easily degraded into semi-supervised learning by just tuning the regularization parameter. Moreover, to select the discriminative SFSR-graph Laplacians, we also introduce the ensemble SFSR-graph Laplacians regularization into SFSR-MSAL, thus further improving the performance of SFSR-MSAL. The validity of our methods including semi-supervised and DA learning are examined by several visual recognition tasks on some benchmark datasets, which demonstrate the superiority of our methods in comparison with other related state-of-the-art algorithms.