Multi-view clustering via spectral embedding fusion

Multi-view learning, such as multi-view feature learning and multi-view clustering, has a wide range of applications in machine learning and pattern recognition. Most previous studies employ the multiple data information from various views to improve the performance of learning. The key problem is to integrate the symbiotic part of the different views or datasets. In practical clustering task, the symbiotic part includes two levels: global structure information and local structure information. However, traditional multi-view clustering methods usually ignore the energy of the local structure information. This paper proposes a novel multi-view clustering model to solve this problem, which simultaneously integrates the global structure information and local structure information of all the views. By integrating the fusion of global spectral embedding and the fusion of spectral manifold embedding from multi-view data, we construct an objective function to find the final fusional embedding and give an iteration method to solve it by using the L2,1 norm. Finally, the K-means clustering method is applied to the obtained final fusional embedding. Extensive experimental results on several real multi-view data sets demonstrate the superior performance of our model.