Paper Info
Title
Discriminative and informative joint distribution adaptation for unsupervised domain adaptation
Abstract
Domain adaptation learning is proposed as an effective technology for leveraging rich supervision knowledge from the related domain(s) to learn a reliable classifier for a new domain. One popular kind of domain adaptation methods is based on feature representation. However, such methods fail to consider the within-class and between-class relations after obtaining the new representation. In addition, they do not consider the negative effects of features that might be redundant or irrelevant to the final classification. To this end, a novel domain-invariant feature learning method based on the maximum margin criterion and sparsity technique for unsupervised domain adaptation is proposed in this paper, referred to as discriminative and informative joint distribution adaptation (DIJDA). Specifically, DIJDA adopts the maximum margin criterion in the adaptation process such that the transformed samples are near to those in the same class but segregated from those in different classes. As a result, the discriminative knowledge referred from source labels can be transferred to target domain effectively. Moreover, DIJDA imposes a row-sparsity constraint on the transformation matrix, which enforces rows of the matrix corresponding to inessential feature attributes to be all zero. Therefore, the most informative feature attributes can be extracted. Compared with several state-of-the-art methods, DIJDA substantially improves the classification results on five widely used benchmark datasets, which demonstrates the effectiveness of the proposed method.
Year
DOI
Venue
2020
10.1016/j.knosys.2020.106394
Knowledge-Based Systems
Keywords
DocType
Volume
Domain adaptation,Joint distribution adaptation,Maximum margin criterion,Row-sparsity
Journal
207
ISSN
Citations 
PageRank 
0950-7051
0
0.34
References 
Authors
0
2
Name
Order
Citations
PageRank
Liran Yang123.40
Ping Zhong24011.34