Abstract Multi-view clustering (MVC), which aims to explore the underlying structure of data by leveraging heterogeneous information different views, has brought along a growth attention. algorithms based on theories have been proposed and extended in various applications. However, most existing MVC are shallow models, learn multi-view mapping low-dimensional representation space directly, igno...

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 ...

Graph-based multi-view clustering aiming to obtain a partition of data across multiple views, has received considerable attention in recent years. Although great efforts have been made for graph-based clustering, it is still challenging fuse characteristics from various views learn common representation clustering. In this paper, we propose novel Consistent Multiple Graph Embedding Clustering f...

Generally, the existing graph-based multi-view clustering models consists of two steps: (1) graph construction; (2) eigen-decomposition on Laplacian matrix to compute a continuous cluster assignment matrix, followed by post-processing algorithm get discrete one. However, both construction and are time-consuming, two-stage process may deviate from directly solving primal problem. To this end, we...

Image clustering is a particularly challenging computer vision task, which aims to generate annotations without human supervision. Recent advances focus on the use of self-supervised learning strategies in image clustering, by first valuable semantics and then representations. These multiple-phase algorithms, however, involve several hyper-parameters transformation functions, are computationall...

Dear Editor, This letter proposes a contrastive consensus graph learning model for multi-view clustering. Graphs are usually built to outline the correlation between multi-model objects in clustering task, and multiview aims learn that integrates spatial property of each view. Nevertheless, most graph-based models merely consider overall structure from all views but neglect local consistency di...

Multi-view clustering has attracted increasing attentions recently by utilizing information from multiple views. However, existing multi-view methods are either with high computation and space complexities, or lack of representation capability. To address these issues, we propose deep embedded collaborative training (DEMVC) in this paper. Firstly, the representations views learned individually ...