Multi-view clustering via simultaneous weighting on views and features

In big data era, more and more data are collected from multiple views, each of which reflect distinct perspectives of the data. Many multi-view data are accompanied by incompatible views and high dimension, both of which bring challenges for multi-view clustering. This paper proposes a strategy of simultaneous weighting on view and feature to discriminate their importance. Each feature of multi-view data is given bi-level weights to express its importance in feature level and view level, respectively. Furthermore, we implements the proposed weighting method in the classical k-means algorithm to conduct multi-view clustering task. An efficient gradient-based optimization algorithm is embedded into k-means algorithm to compute the bi-level weights automatically. Also, the convergence of the proposed weight updating method is proved by theoretical analysis. In experimental evaluation, synthetic datasets with varied noise and missing-value are created to investigate the robustness of the proposed approach. Then, the proposed approach is also compared with five state-of-the-art algorithms on three real-world datasets. The experiments show that the proposed method compares very favourably against the other methods.