Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/62208
Title: Robust visual knowledge transfer via extreme learning machine-based domain adaptation
Authors: Zhang, L
Zhang, D 
Keywords: Cross-domain learning
Domain adaptation
Extreme learning machine
Knowledge adaptation
Issue Date: 2016
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on image processing, 2016, v. 25, no. 10, 7539280, p. 4959-4973 How to cite?
Journal: IEEE transactions on image processing 
Abstract: We address the problem of visual knowledge adaptation by leveraging labeled patterns from source domain and a very limited number of labeled instances in target domain to learn a robust classifier for visual categorization. This paper proposes a new extreme learning machine (ELM)-based cross-domain network learning framework, that is called ELM-based Domain Adaptation (EDA). It allows us to learn a category transformation and an ELM classifier with random projection by minimizing the l21-Norm of the network output weights and the learning error simultaneously. The unlabeled target data, as useful knowledge, is also integrated as a fidelity term to guarantee the stability during cross-domain learning. It minimizes the matching error between the learned classifier and a base classifier, such that many existing classifiers can be readily incorporated as the base classifiers. The network output weights cannot only be analytically determined, but also transferrable. In addition, a manifold regularization with Laplacian graph is incorporated, such that it is beneficial to semisupervised learning. Extensively, we also propose a model of multiple views, referred as MvEDA. Experiments on benchmark visual datasets for video event recognition and object recognition demonstrate that our EDA methods outperform the existing cross-domain learning methods.
URI: http://hdl.handle.net/10397/62208
ISSN: 1057-7149
EISSN: 1941-0042
DOI: 10.1109/TIP.2016.2598679
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