Multi-graph Fusion for Multi-view Spectral Clustering

For example, a person can be uniquely identified in terms of face, fingerprint, iris, and signature; an image can be described by different kinds of descriptors: SIFT, HOG, and LBP, where SIFT is robust to image illumination, noise, and rotation, HOG is sensitive to marginal information, while LBP is a powerful texture feature; the same document can be represented in different languages. Different views can capture distinct perspectives of data. Numerous real-world applications have benefited from multi-view data by leveraging the complementary information [5, 6, 7, 8, 9]. Thus, multi-view learning has become an important research field [10, 11]. As an important ingredient of multi-view learning, multi-view clustering has been widely investigated to identify underlying structures in multi-view data in an unsupervised way [12, 13]. Although each view contains different fractional information, they together admit the same clustering structure. Simply concatenating all features into a single view and then employing a clustering algorithm on this single view data might not obtain better performance than traditional methods which use single view separately [14, 11]. In the past decade, plenty of advanced multi-view clustering algorithms have been proposed and they perform effectively by considering the diversity and complementarity of different views.