Multi-View Maximum Margin Clustering With Privileged Information Learning

Maximum margin clustering (MMC) is a typical clustering method which aims to maximize the margin between different clusters. However, in practical applications, a data object may be represented by multiple feature sets (views), with each feature set representing different information of the underlying data. The traditional MMC methods can handle only the data from a single view and are unable to utilize the multi-view data to enhance the clustering model. In multi-view clustering, there are two basic principles: the consensus principle and complementarity principle. Most multi-view clustering methods implement mainly the consensus principle, while the complementarity principle has not been sufficiently taken into account. Distinguished from the existing methods, M^3 CP introduces the idea of privileged information learning into multi-view clustering and implements both of the consensus principle and complementarity principle. Based on privileged information learning, M^3 CP embodies the complementarity principle by considering one view as the main learning information and the other views as the privileged information, so that multiple views can provide information to complement each other. The derived learning problem is then solved by applying the constrained concave–convex procedure and cutting plane techniques. By employing these techniques, the computational time of M^3 CP is able to scale linearly with respect to the dataset size. Numerical experiments on real-life multi-view datasets demonstrate that M^3 CP is able to achieve better clustering accuracy and meanwhile needs less computational time, compared to state-of-the-art multi-view clustering methods.