Discriminative kernel transfer learning via l2,1-norm minimization

In this paper, we propose a l
2,1
-norm based discriminative robust transfer learning (DKTL) method for domain adaptation tasks. The key idea is to simultaneously learn discriminative subspaces by using the proposed domain-class-consistency (DCC) metric, and the representation based robust transfer model between source domain and target domain via l
21
-norm minimization. The DCC metric includes two parts: domain-consistency used to measure the between-domain distribution discrepancy and class-consistency used to measure the within-domain class separability. The objective of transfer learning is to maximize the proposed metric, while for easily formulating this metric in model, we propose to minimize the domain-class-inconsistency, such that both domain distribution mismatch and class inseparability are well addressed. Two advantages of the proposed method are that on one hand the robust sparse coding selects a few valuable source data with noises (outliers) removed during knowledge transfer, and the proposed DCC metric can help to pursue discriminative subspaces of different domains for classification based transfer learning tasks. Extensive experiments demonstrate the superiority of the proposed method over other state-of-the-art domain adaptation methods.