论文标题
通过图正则化的歧视性半监督多视觉矩阵分解
Discriminatively Constrained Semi-supervised Multi-view Nonnegative Matrix Factorization with Graph Regularization
论文作者
论文摘要
近年来，半监督的多视图非负矩阵分解（MVNMF）算法已经实现了多视图聚类的有希望的性能。尽管大多数半监督的MVNMF未能有效地考虑群集之间的歧视性信息，并同时从多个视图中进行了特征对齐。在本文中，提出了一种新颖的歧视性约束半监督的多视图非负矩阵分解（DCS^2MVNMF）。具体而言，为每种视图的辅助矩阵引入了判别加权矩阵，从而增强了类间的区别。同时，使用标签和几何信息构建了一个新的图形正则化。此外，我们设计了一种新的功能量表归一化策略，以对齐多个视图并完成相应的迭代优化方案。在几个现实世界多视图数据集上进行的广泛实验证明了该方法的有效性。
In recent years, semi-supervised multi-view nonnegative matrix factorization (MVNMF) algorithms have achieved promising performances for multi-view clustering. While most of semi-supervised MVNMFs have failed to effectively consider discriminative information among clusters and feature alignment from multiple views simultaneously. In this paper, a novel Discriminatively Constrained Semi-Supervised Multi-View Nonnegative Matrix Factorization (DCS^2MVNMF) is proposed. Specifically, a discriminative weighting matrix is introduced for the auxiliary matrix of each view, which enhances the inter-class distinction. Meanwhile, a new graph regularization is constructed with the label and geometrical information. In addition, we design a new feature scale normalization strategy to align the multiple views and complete the corresponding iterative optimization schemes. Extensive experiments conducted on several real world multi-view datasets have demonstrated the effectiveness of the proposed method.
