Wang Xiumei, Zhang Tianzhen, Gao Xinbo
IEEE Trans Cybern. 2019 Sep;49(9):3333-3346. doi: 10.1109/TCYB.2018.2842052. Epub 2018 Jun 21.
As we all know, multiview clustering has become a hot topic in machine learning and pattern recognition. Non-negative matrix factorization (NMF) has been one popular tool in multiview clustering due to its competitiveness and interpretation. However, the existing multiview clustering methods based on NMF only consider the similarity of intra-view, while neglecting the similarity of inter-view. In this paper, we propose a novel multiview clustering algorithm, named multiview clustering based on NMF and pairwise measurements, which incorporates pairwise co-regularization and manifold regularization with NMF. In the proposed algorithm, we consider the similarity of the inter-view via pairwise co-regularization to obtain the more compact representation of multiview data space. We can also obtain the part-based representation by NMF and preserve the locally geometrical structure of the data space by utilizing the manifold regularization. Furthermore, we give the theoretical proof that the objective function of the proposed algorithm is convergent for multiview clustering. Experimental results show that the proposed algorithm outperforms the state-of-the-arts for multiview clustering.
众所周知，多视图聚类已成为机器学习和模式识别中的一个热门话题。非负矩阵分解（NMF）因其竞争力和可解释性，一直是多视图聚类中一种流行的工具。然而，现有的基于NMF的多视图聚类方法仅考虑视图内的相似性，而忽略了视图间的相似性。在本文中，我们提出了一种新颖的多视图聚类算法，即基于NMF和成对测量的多视图聚类算法，该算法将成对协同正则化和流形正则化与NMF相结合。在所提出的算法中，我们通过成对协同正则化考虑视图间的相似性，以获得多视图数据空间更紧凑的表示。我们还可以通过NMF获得基于部分的表示，并利用流形正则化保留数据空间的局部几何结构。此外，我们给出了所提出算法的目标函数对于多视图聚类是收敛的理论证明。实验结果表明，所提出的算法在多视图聚类方面优于现有技术。
