A Domain Adaptation Approach for Multistream Classification

Abstract

In this paper, we formulate cross-domain multistream classification as a domain adaptation problem. Then we propose a novel algorithm that utilizes low-rank representation and graph embedding to preserve data structures, which benefits in dealing with concept drifts and concept revolution. In addition, we deploy MMD metric to minimize the distribution discrepancy between the source data stream and the target data stream. Experiment results on Office+Caltech dataset with DeCAF $$:6$$ features verified the effectiveness of our algorithm.