This study investigates the problem of multi-view subspace clustering, goal which is to explore underlying grouping structure data collected from different fields or measurements. Since do not always comply with linear models in many real-world applications, most existing clustering methods based on shallow may fail practice. Furthermore, graph information usually ignored methods. To address af...

As a class of effective methods for incomplete multi-view clustering, graph-based algorithms have recently drawn wide attention. However, most them could use further improvement regarding the following aspects. First, in some models, all views are forced to share common similarity graph regardless severe consistency degeneration due views. Next, construction and cluster analysis sometimes perfo...

Due to the existence of various views or representations in many real-world data, multi-view learning has drawn much attention recently. Multi-view spectral clustering methods based on similarity matrixes or graphs are pretty popular. Generally, these algorithms learn informative graphs by directly utilizing original data. However, in the real-world applications, original data often contain noi...

More and more multi-view data which can capture rich information from heterogeneous features are widely used in real world applications. How to integrate different types of features, and how to learn low dimensional and discriminative information from high dimensional data are two main challenges. To address these challenges, this paper proposes a novel multi-view feature learning framework, wh...

Existing multi-view clustering algorithms require that the data is completely or partially mapped between each pair of views. However, this requirement could not be satisfied in most practical settings. In this paper, we tackle the problem of multi-view clustering for unmapped data in the framework of NMF based clustering. With the help of inter-view constraints, we define the disagreement betw...

Automatic social circle detection in ego-networks is becoming a fundamentally important task for social network analysis, which can be used for privacy protection or interest group recommendation. So far, most studies focused on how to detect overlapping circles or how to perform detection using both network structure and its node profiles. This paper asks an orthogonal research question: how t...

The main aim of this paper is to demonstrate the performance of multi-classifiers fusion based on fuzzy clustering with ambiguity. The problem is seen from the multi-decision point of view (i.e. several classification modules). Each classification module is specialized on a particular region of the features space. These regions are obtained by fuzzy clustering and constitute the original data s...

Low-Rank Representation (LRR) is arguably one of the most powerful paradigms for Multi-view spectral clustering, which elegantly encodes the multi-view local graph/manifold structures into an intrinsic low-rank self-expressive data similarity embedded in high-dimensional space, to yield a better graph partition than their single-view counterparts. In this paper we revisit it with a fundamentall...