NDLI: Co-regularization for classification

Content Provider	IEEE Xplore Digital Library
Author	Yang Li Dapeng Tao Weifeng Liu Yanjiang Wang
Copyright Year	2014
Description	Author affiliation: Coll. of Inf. & Control Eng., China Univ. of Pet. (East China), Qingdao, China (Yang Li; Weifeng Liu; Yanjiang Wang) \|\| Shenzhen Inst. of Adv. Technol., Shenzhen, China (Dapeng Tao)
Abstract	Semi-supervised learning algorithms that combine labeled and unlabeled data receive significant interests in recent years and are successfully deployed in many practical data mining applications. Manifold regularization, one of the most representative works, tries to explore the geometry of the intrinsic data probability distribution by penalizing the classification function along the implicit manifold. Although existing manifold regularization, including Laplacian regularization (LR) and Hessian regularization (HR), yields significant benefits for partially labeled classification, it is observed that LR suffers from the poor generalization and HR exhibits the characteristic of instability, both manifold regularization could not accurately reflect the ground-truth. To remedy the problems in single manifold regularization and approximate the intrinsic manifold, we propose Manifold Regularized Co-Training (Co-Re) framework, which combines the manifold regularization (LR and HR) and the algorithm co-training. Extensive experiments on the USAA video dataset are conducted and validate the effectiveness of Co-Re by comparing it with baseline manifold regularization algorithms.
Starting Page	218
Ending Page	222
File Size	514256
Page Count	5
File Format	PDF
e-ISBN	9781479953530
DOI	10.1109/SPAC.2014.6982688
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-10-18
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Manifolds Decision support systems Hessian regularization Laplace equations Semi-supervised learning Information processing Hafnium Laplacian regularization Approximation algorithms Manifold regularization Erbium Co-Training
Content Type	Text
Resource Type	Article

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