Multi-view clustering has received a lot of attentions in data mining recently. Though plenty works have been investigated on this topic, it is still severe challenge due to the complex nature multiple heterogeneous features. Particularly, existing multi-view algorithms fail consider topological structure data, which essential for manifold. In paper, we propose exploit implied manifold by learn...

Real data are often with multiple modalities or coming from multiple channels, while multi-view clustering provides a natural formulation for generating clusters from such data. Previous studies assumed that each example appears in all views, or at least there is one view containing all examples. In real tasks, however, it is often the case that every view suffers from the missing of some data ...

Multi-View Clustering (MVC) aims to find the cluster structure shared by multiple views of a particular dataset. Existing MVC methods mainly integrate the raw data from different views, while ignoring the high-level information. Thus, their performance may degrade due to the conflict between heterogeneous features and the noises existing in each individual view. To overcome this problem, we pro...

Assuming that actors u and v have r common neighbors in a social network we are interested in how likely is that u and v are adjacent. This question is addressed by studying the collection of conditional probabilities, denoted cl(r), r = 0, 1, 2, . . . , that two randomly chosen actors of the social network are adjacent, given that they have r common neighbors. The function r → cl(r) describes ...

Multi-view clustering methods have been a focus in recent years because of their superiority performance. However, typical traditional multi-view algorithms still shortcomings some aspects, such as removal redundant information, utilization various views and fusion features. In view these problems, this paper proposes new method, low-rank subspace based on adaptive graph regularization. We cons...