Semi-supervised learning models for multiview data are important in image classification tasks, since heterogeneous features are easy to obtain and semi-supervised schemes are economical and effective. To model the view importance, conventional graph-based multiview learning models learn a linear combination of views while assuming a priori weights distribution. In this paper, we present a novel structural regularized semi-supervised model for multiview data, termed Adaptive MUltiview SEmi-supervised model (AMUSE). Our new model learns weights from a priori graph structure, which is more reasonable than weight regularization. Theoretical analysis reveals the significant difference between AMUSE and the prior arts. An efficient optimization algorithm is provided to solve the new model. Experimental results on six real-world data sets demonstrate the effectiveness of the structural regularized weights learning scheme.

中文翻译：

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用于图像分类的多视图半监督学习模型

多视图数据的半监督学习模型在图像分类任务中很重要，因为异构特征很容易获得，而且半监督方案经济有效。为了对视图重要性建模，传统的基于图的多视图学习模型在假设先验权重分布的同时学习视图的线性组合。在本文中，我们提出了一种用于多视图数据的新型结构正则化半监督模型，称为自适应多视图半监督模型 (AMUSE)。我们的新模型从先验图结构中学习权重，这比权重正则化更合理。理论分析揭示了 AMUSE 与现有技术之间的显着差异。提供了一种有效的优化算法来求解新模型。