Robust domain adaptation image classification via sparse and low rank representation

Domain adaptation image classification addresses the problem of adapting the image distribution of the source domain to the target domain for an effective learning task, where the classification objective is intended but the data distributions are different. However, corrupted data (e.g. noise and outliers, which exist universally in real-world domains) can cause significant deterioration of the practical performance of existing methods in cross-domain image classification. This motivates us to propose a robust domain adaptation image classification method with sparse and low rank representation. Specifically, we first obtain an optimal Domain Adaptation Sparse and Low Rank Representation (DASLRR) for all the data from both domains by incorporating a distribution adaptation regularization term, which is expected to minimize the distribution discrepancy between the source and target domain, into the existing low rank and sparse representation objective function. Formulating an optimization problem that combines the objective function of the sparse and low rank representation, constrained by distribution adaptation and local consistency, we propose an algorithm that alternates between obtaining an effective dictionary, while preserving the DASLRR to make the new representations robust to the distribution difference. Based on the obtained DASLRR, we then provide a flexible semi-supervised learning framework, which can propagate the labels of labeled data from both domains to unlabeled data from In-Sample as well as Out-of-Sample datasets by simultaneously learning a prediction label matrix and a classifier model. The proposed method can capture the global mixture of the clustering structure (by the sparseness and low rankness) and the locally consistent structure (by the local graph regularization) as well as the distribution difference (by the distribution adaptation) of the domains data. Hence, the proposed method is robust for accurately classifying cross-domain images that may be corrupted by noise or outliers. Extensive experiments demonstrate the effectiveness of our method on several types of images and video datasets. (C) 2015 Elsevier Inc. All rights reserved.