A Survey on Multi-View Clustering

Clustering [1] is a paradigm to classify the subjects into several groups based on their similarity information. As we know that clustering is a fundamental task in machine learning, pattern recognition and data mining fields and it has widespread applications. With the obtained groups by clustering methods, further analysis tasks can be conducted to achieve different ultimate goals. However, traditional clustering methods only use one feature set or one view information of the subjects while multiple feature sets or multiple view information of these subjects are available. The subjects of interest with multiple feature sets or multiple view information are the so called multi-view data. Multi-view data are very common in real-world applications due to the innate properties, or collecting from different sources. For instance, a web page can be described by the words appearing on the web page itself and the words underlying all links pointing to the web page from other pages in nature. In multimedia content understanding, the multimedia segments can be simultaneously described by their video signals from visual camera and audio signals from voice recorder devices. The existence of such multi-view data raised the interest of multi-view learning [2], [3], [4], which has been extensively studied in semi-supervised setting.