Unsupervised Domain Adaptation Method Based on Discriminant Sample Selection

In order to solve the problem that low classification accuracy caused by the different distribution of training set and test set, an unsupervised domain adaptation method based on discriminant sample selection (DSS) is proposed. DSS projects the samples of different domains onto a same subspace to reduce the distribution discrepancy between the source domain and the target domain, and weights the source domain instances to make the samples more discriminant. Different from the previous method based on the probability density estimation of samples, DSS tries to obtain the sample weights by solving a quadratic programming problem, which avoids the distribution estimation of samples and can be applied to any fields without suffering from the dimensional trouble caused by high-dimensional density estimation. Finally, DSS congregates the same classes by minimizing the intra-class distance. Experimental results show that the proposed method improves the classification accuracy and robustness.