Evolutionary multi-objective optimization for multi-view clustering

In some real-world applications, multiple measuring methods are often employed to extract multiple feature groups of data, yielding multi-view data. The main challenge of multiview clustering is to find a suitable way of simultaneously exploiting the complementary information of all views, considering the view conflicts arose by different measures. For perspective of optimization, previous multi-view clustering studies applied weighted sum method to represent degree of conflict and treated it as a weighted sum single-objective optimization problem. In this work, we formatted multi-view clustering as a multi-objective optimization problem, in which each view is regarded as a totally independent feature subset. The clustering objective function in each view is one of the multiple objectives. Five popular multi-objective evolutionary algorithms (MOEAs), i.e., NSGA-II, SPEA2, MOEA/D, SMS-EMOA and NSGA-III, were used to solve the induced multi-objective problem. Six real-world multi-view datasets were used to evaluate the proposed method and the experimental results showed that SPEA2 significantly outperformed the other MOEAs according to three performance evaluation indices.