Discriminative subspace matrix factorization for multiview data clustering

In a real-world scenario, an object is easily considered as features combined by multiple views in reality. Thus, multiview features can be encoded into a unified and discriminative framework to achieve satisfactory clustering performance. An increasing number of algorithms have been proposed for multiview data clustering. However, existing multiview methods have several drawbacks. First, most multiview algorithms focus only on origin data in high dimension directly without the intrinsic structure in the relative low-dimensional subspace. Spectral and manifold-based methods ignore pseudo-information that can be extracted from the optimization process. Thus, we design an unsupervised nonnegative matrix factorization (NMF)-based method called discriminative multiview subspace matrix factorization (DMSMF) for clustering. We provide the following contributions. (1) We extend linear discriminant analysis and NMF to a multiview version and connect them to a unified framework to learn in the discriminant subspace. (2) We propose a multiview manifold regularization term and discriminant multiview manifold regularization term that instruct the regularization term to discriminate different classes and obtain the geometry st ructure from the low-dimensional subspace. (3) We design an effective optimization algorithm with proven convergence to obtain an optimal solution procedure for the complex model. Adequate experiments are conducted on multiple benchmark datasets. Finally, we demonstrate that our model is superior to other comparable multiview data clustering algorithms. (C) 2020 Elsevier Ltd. All rights reserved.

Automated summary

In this study, a method called DMSMF is proposed to address the drawbacks of existing multiview data clustering methods. By extending linear discriminant analysis and NMF to a multiview version, and incorporating novel regularization terms and optimization algorithms, satisfactory clustering performance is achieved.