Exploring the information among multiple views usually leads to more promising clustering performance. Most existing multi-view clustering algorithms perform clustering separately: first extracts multiple handcrafted features or deep features, then conducts traditional clustering such as spectral clustering or K-means. However, the learned features may not often work well for clustering. To overcome this problem, we propose the Deep Multi-View Clustering via Multiple Embedding (DMVC-ME), which learns deep embedded features, multi-view fusion mechanism and clustering assignment simultaneously in an end-to-end manner. Specifically, we adopt a KL divergence to refine the soft clustering assignment with the help of a multi-view fused target distribution. The parameters are updated via an efficient alternative optimization scheme. As a result, more clustering-friendly features can be learned and the complementary traits among different views can be well captured. We demonstrate the effectiveness of our approach on several challenging image datasets, where significant superiority can be found over single/multi-view baselines and the state-of-the-art multi-view clustering methods.
View on arXiv