Multi-View Local Learning
Multi-View Local Learning
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发表时间：
2008-07
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通讯作者：
Dan Zhang;Fei Wang;Changshui Zhang;Ta-Hsin Li
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作者：
Dan Zhang;Fei Wang;Changshui Zhang;Ta-Hsin Li
本地学习的概念，即，基于其近邻对特定示例进行分类，近来已成功地应用于许多半监督和聚类问题。然而，到目前为止开发的局部学习方法都是针对单视图问题设计的。事实上，在许多真实世界的应用程序中，示例由多组特征表示。本文将局部学习的思想扩展到多视图问题，为每个示例设计了多视图局部模型，并提出了多视图局部学习正则化（MVLL-Reg）矩阵。给出了它的线性形式和核形式。实验证明了该方法的优越性，在几个国家的最先进的。
The idea of local learning, i.e., classifying a particular example based on its neighbors, has been successfully applied to many semi-supervised and clustering problems recently. However, the local learning methods developed so far are all devised for single-view problems. In fact, in many real-world applications, examples are represented by multiple sets of features. In this paper, we extend the idea of local learning to multi-view problem, design a multi-view local model for each example, and propose a Multi-View Local Learning Regularization (MVLL-Reg) matrix. Both its linear and kernel version are given. Experiments are conducted to demonstrate the superiority of the proposed method over several state-of-the-art ones.