Canonical correlation analysis (CCA) is a cost insensitive method. It assumes the same loss for different classification errors and aims to attain a low error rate by maximizing the cross-view correlation. However, in some real-world applications, different classification errors will lead to unequal misclassification losses. In addition, in practice, only limited cost label information is available in training set due to the expensive costs of labelling. This paper aims to perform label propagation with CCA in a unified cost-sensitive learning framework. By learning jointly, both the label propagation and CCA can feed back to each other. Thus, more discriminative and cost-sensitive projections will be learned for feature fusion. We test the proposed method on the cost-sensitive application of door-locker system based on multi-view face recognition. The results in comparison with eight label propagation methods, eleven CCA related methods and eight cost-sensitive single-view methods demonstrate its effectiveness.

中文翻译：

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半监督多视图学习的成本敏感典型相关分析

典型相关分析 (CCA) 是一种成本不敏感的方法。它假设不同分类错误的损失相同，并旨在通过最大化交叉视图相关性来获得低错误率。然而，在一些实际应用中，不同的分类错误会导致不等的误分类损失。此外，在实践中，由于标签成本昂贵，训练集中只有有限的成本标签信息可用。本文旨在在统一的成本敏感学习框架中使用 CCA 进行标签传播。通过联合学习，标签传播和 CCA 可以相互反馈。因此，将为特征融合学习更具辨别力和成本敏感的预测。我们在基于多视图人脸识别的门锁系统成本敏感应用上测试了所提出的方法。