Discriminative maximum mean and covariance discrepancy based domain adaptation for cross-domain image classification

Unsupervised domain adaptation is an effective technology for cross-domain image classification which leverages the rich labeled source domain data to build an accurate classifier for the unlabeled target domain data. To effectively evaluate the distribution discrepancy between domains, the maximum mean discrepancy (MMD) has been widely used for its nonparametric advantage. Recently, a new distribution discrepancy metric named maximum mean and covariance discrepancy (MMCD) was proposed, which combines MMD with the maximum covariance discrepancy (MCD). Since MCD could evaluate the second order statistics in reproducing kernel Hilbert space, the MMCD metric can capture more information than using MMD alone and give better results. However, most existing MMCD based methods generally focus on adapting the marginal and class-conditional distributions between domains but ignore exploring discriminative information offered by the labeled source domain data. To overcome this shortcoming, in this paper we propose a discriminative MMCD based domain adaptation (DMcDA) method, which could simultaneously reduce the MMCD of the marginal and class-conditional distributions between domains and minimize the intra-class scatter of the source domain data. Moreover, a kernel version of DMcDA is also derived. Comprehensive experiments are carried out on two cross-domain image classification problems, and the results demonstrate the effectiveness of the proposed DMcDA method.