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.

Highlights

  • The rapid development of Internet technology and computer hardware has resulted in an exponential increase in the quantity of data uploaded and shared on media platforms [1] [2]

  • One of the earliest multi-view learning schemes was co-training, in which two classifiers are alternately trained on two distinct views in order to maximize the mutual agreement between the two views of unlabeled data [5]

  • Hessians can properly exploit the local distribution geometry of the underlying data manifold [20] [21], and Hessian regularization can significantly boost the generalizability of a classifier, especially when only a small number of labeled examples exist with a large number of unlabeled examples

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Summary

Introduction

The rapid development of Internet technology and computer hardware has resulted in an exponential increase in the quantity of data uploaded and shared on media platforms [1] [2] Processing these data presents a major challenge to machine learning, especially since most of the data are unlabeled and are described by multiple representations in different computer vision applications [3] [4]. Hessians can properly exploit the local distribution geometry of the underlying data manifold [20] [21], and Hessian regularization can significantly boost the generalizability of a classifier, especially when only a small number of labeled examples exist with a large number of unlabeled examples. The experimental results demonstrate that the proposed method outperforms the baseline algorithms

Method Overview
Hessian regularization
Experiments
Conclusion
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