title: Hessian-regularized co-training for social activity recognition.
abstract: Co-training is a major multi-view learning paradigm that alternately trains two classifiers on two distinct views and maximizes the mutual agreement on the two-view unlabeled data. Traditional co-training algorithms usually train a learner on each view separately and then force the learners to be consistent across views. Although many co-trainings have been developed, it is quite possible that a learner will receive erroneous labels for unlabeled data when the other learner has only mediocre accuracy. This usually happens in the first rounds of co-training, when there are only a few labeled examples. As a result, co-training algorithms often have unstable performance. In this paper, Hessian-regularized co-training is proposed to overcome these limitations. Specifically, each Hessian is obtained from a particular view of examples; Hessian regularization is then integrated into the learner training process of each view by penalizing the regression function along the potential manifold. Hessian can properly exploit the local structure of the underlying data manifold. Hessian regularization significantly boosts the generalizability of a classifier, especially when there are a small number of labeled examples and a large number of unlabeled examples. To evaluate the proposed method, extensive experiments were conducted on the unstructured social activity attribute (USAA) dataset for social activity recognition. Our results demonstrate that the proposed method outperforms baseline methods, including the traditional co-training and LapCo algorithms.
authors.affiliation: College of Information and Control Engineering, China University of Petroleum (East China), Qingdao, Shandong, China.
authors.forename: Weifeng
authors.initials: W
authors.lastname: Liu
authors.affiliation: College of Information and Control Engineering, China University of Petroleum (East China), Qingdao, Shandong, China.
authors.forename: Yang
authors.initials: Y
authors.lastname: Li
authors.affiliation: Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China; The Chinese University of Hong Kong, Hong Kong, China.
authors.forename: Xu
authors.initials: X
authors.lastname: Lin
authors.affiliation: Centre for Quantum Computation and Intelligent Systems, and Faculty of Engineering and Information Technology, University of Technology, Sydney, Ultimo, New South Wales, Australia.
authors.forename: Dacheng
authors.initials: D
authors.lastname: Tao
authors.affiliation: College of Information and Control Engineering, China University of Petroleum (East China), Qingdao, Shandong, China.
authors.forename: Yanjiang
authors.initials: Y
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D007858:Learning; D010363:Pattern Recognition, Automated