Continual Multi-view Clustering

With the increase of multimedia applications, data are often collected from multiple sensors or modalities, encouraging the rapid development of multi-view (also called multi modal) clustering technique. As a representative, late fusion multi-view clustering algorithm has attracted extensive attention due to its low computation complexity yet promising performance. However, most of them deal with the clustering problem in which all data views are available in advance, and overlook the scenarios where data observations of new views are accumulated over time. To solve this issue, we propose a continual approach on the basis of late fusion multi-view clustering framework. In specific, it only needs to maintain a consensus partition matrix and update knowledge with the incoming one of a new data view rather than keep all of them. This benefits a lot by preventing the previously learned knowledge from recomputing over and over again, saving a large amount of computation resource/time and labor force. Nevertheless, we design an alternate and convergent strategy to solve the resultant optimization problem. Also, the proposed algorithm shows excellent clustering performance and time/space efficiency in the experiment.