Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/75631
Title: Domain class consistency based transfer learning for image classification across domains
Authors: Zhang, L
Yang, J
Zhang, D 
Keywords: Transfer learning
Representation learning
Subspace learning
Kernel learning
Domain adaptation
Issue Date: 2017
Publisher: Elsevier
Source: Information sciences, 2017, v. 418, p. 242-257 How to cite?
Journal: Information sciences 
Abstract: Distribution mismatch between the modeling data and the query data is a known domain adaptation issue in machine learning. To this end, in this paper, we propose a l(2,1)-norm based discriminative robust kernel transfer learning (DKTL) method for high-level recognition tasks. The key idea is to realize robust domain transfer by simultaneously integrating domain-class-consistency (DCC) metric based discriminative subspace learning, kernel learning in reproduced kernel Hilbert space, and representation learning between source and target domain. The DCC metric includes two properties: domain-consistency used to measure the between-domain distribution discrepancy and class-consistency used to measure the within-domain class separability. The essential objective of the proposed transfer learning method is to maximize the DCC metric, which is equivalently to minimize the domain-class-inconsistency (DCIC), such that domain distribution mismatch and class inseparability are well formulated and unified simultaneously. The merits of the proposed method include (1) the robust sparse coding selects a few valuable source data with noises (outliers) removed during knowledge transfer, and (2) the proposed DCC metric can pursue more discriminative subspaces of different domains. As a result, the maximum class separability is also well guaranteed. Extensive experiments on a number of visual datasets demonstrate the superiority of the proposed method over other state-of-the-art domain adaptation and transfer learning methods.
URI: http://hdl.handle.net/10397/75631
ISSN: 0020-0255
EISSN: 1872-6291
DOI: 10.1016/j.ins.2017.08.034
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