Hessian-regularized co-training for social activity recognition

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 co...

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Bibliographic Details
Published in:PloS one 2014-09, Vol.9 (9), p.e108474-e108474
Main Authors: Liu, Weifeng, Li, Yang, Lin, Xu, Tao, Dacheng, Wang, Yanjiang
Format: Article
Language:English
Subjects:
Activity recognition
Algorithms
Artificial Intelligence
Classifiers
Clustering
Computer and Information Sciences
Datasets
Engineering
Geometry
Hypotheses
International conferences
Learning
Manifolds
Multimedia
Pattern Recognition, Automated
Regularization
Social interactions
Training
Weddings
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Summary: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.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0108474