Heterogeneous domain adaptation with label and structural consistency

Heterogeneous domain adaptation is a challenging problem due to the fact that it requires generalizing a learning model across training data and testing data with different distributions and features. To alleviate the difficulty of this task, most researchers usually perform some data preprocessing operations. However, such operations may lead to the loss of shareable information before domain adaptation. Moreover, most current work neglects the structural information between data, which is crucial for classification. To overcome the limitations mentioned above, we propose a novel algorithm, named as heterogeneous discriminative features learning and label propagation (HDL), which includes i) features learning with label consistency through two domain-specific projections, and ii) label propagation through exploiting structural information of data. Notably, each of the two sides reinforces each other. For each objective function, the corresponding analytical solutions are presented. Comprehensive experimental evidence on a large number of text categorization, image sclassification and text to image recognition datasets verifies the effectiveness and efficiency of the proposed approach over several state-of-the-art methods.