Jointly discriminative projection and dictionary learning for domain adaptive collaborative representation-based classification

In recent years, collaborative representation-based classification (CRC) methods have shown impressive performance in many recognition tasks. However, when the training data have different distributions with the testing data, the performance of CRC will be degraded significantly. On the other hand, con-catenating training data from different sources as a single data set will affect the performance of CRC, as the shift exists between the different source domains. To address these problems, in this paper, we propose a Jointly Discriminative projection and Dictionary learning for domain adaptive Collaborative Representation-based Classification method (JD(2)-CRC). As the distributions of different source domains may be dissimilar, the data from all domains are projected into a common feature subspace where the latent shared structures can be found. Then a compact dictionary is learned to represent the projected data well. To find the most suitable projection matrices and dictionary for CRC, we design the objective function of JD(2)-CRC,according to the classification rule of CRC in feature subspace, which minimizes the ratio of within-class reconstruction errors over between-class reconstruction errors. Different to traditional optimization methods, an effective optimization procedure is presented based on gradient descent. Thus, the obtained collaborative representations have a better discriminability and suit the classification rule of CRC well. The experimental results demonstrate that the proposed method can achieve superior performance against other state-of-the-art methods. (C) 2019 Elsevier Ltd. All rights reserved.