Multi-view clustering has attracted much attention thanks to the capacity of multi-source information integration. Although numerous advanced methods have been proposed in past decades, most them generally overlook significance weakly-supervised and fail preserve feature properties multiple views, thus resulting unsatisfactory performance. To address these issues, this paper, we propose a novel...

Multi-view clustering is an important research topic due to its capability utilize complementary information from multiple views. However, there are few methods consider the negative impact caused by certain views with unclear structures, resulting in poor multi-view performance. To address this drawback, we propose self-supervised discriminative feature learning for deep m...

In past decade, more and more data are collected from multiple sources or represented by multiple views, where different views describe distinct perspectives of the data. Although each view could be individually used for finding patterns by clustering, the clustering performance could be more accurate by exploring the rich information among multiple views. Several multi-view clustering methods ...

Learning from multi-view data is important in many applications. In this paper, we propose a novel convex subspace representation learning method for unsupervised multi-view clustering. We first formulate the subspace learning with multiple views as a joint optimization problem with a common subspace representation matrix and a group sparsity inducing norm. By exploiting the properties of dual ...

The ways of placing decision making units (DMUs) in certain clusters are found as a subject in statistics, these ways usually are heuristic. The proposed clustering approach in this article considers preferences of DMUs. This study applies Data Envelopment Analysis (DEA) DMUs are clustered by solving multi-objective linear problem (MOLP) and by considering preferences of each DMU at production ...

In recent years, multi-view clustering research has attracted considerable attention because of the rapidly growing demand for unsupervised analysis data in practical applications. Despite significant advances clustering, two challenges still need to be addressed, i.e., how make full use consistent and complementary information multiple views discriminate contributions different features same v...

In many real-world applications, we have access to multiple views of the data, each of which characterizes the data from a distinct aspect. Several previous algorithms have demonstrated that one can achieve better clustering accuracy by integrating information from all views appropriately than using only an individual view. Owing to the effectiveness of spectral clustering, many multi-view clus...