A Co-Regularization Approach to Semi-supervised Learning with Multiple Views
A Co-Regularization Approach to Semi-supervised Learning with Multiple Views
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发表时间：
2005
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通讯作者：
Vikas Sindhwani;P. Niyogi;M. Belkin
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作者：
Vikas Sindhwani;P. Niyogi;M. Belkin
联合训练算法使用多个视图中的未标记样本来引导每个视图中的分类器，通常是以贪婪的方式，并在视图无关和兼容性的假设下操作。在本文中，我们提出了一种协同正则化框架，通过多视图正则化的形式在每个视图中学习分类器。我们在这个框架内提出了基于对已标记和未标记示例的一致性和光滑性的优化度量的算法。这些算法自然地扩展了支持向量机和正则化最小二乘等用于多视点半监督学习的标准正则化方法，继承了它们在高维分类问题上的优点和适用性。一项实证研究证实了这种方法的前景。
The Co-Training algorithm uses unlabeled examples in multiple views to bootstrap classifiers in each view, typically in a greedy manner, and operating under assumptions of view-independence and compatibility. In this paper, we propose a Co-Regularization framework where classifiers are learnt in each view through forms of multi-view regularization. We propose algorithms within this framework that are based on optimizing measures of agreement and smoothness over labeled and unlabeled examples. These algorithms naturally extend standard regularization methods like Support Vector Machines (SVM) and Regularized Least squares (RLS) for multi-view semi-supervised learning, and inherit their benefits and applicability to high-dimensional classification problems. An empirical investigation is presented that confirms the promise of this approach.