Self-paced latent embedding space learning for multi-view clustering

Multi-view clustering (MVC) can integrate the complementary information between different views to remarkably improve clustering performance. However, the existing methods suffer from the following drawbacks: (1) multi-view data are often lying on high-dimensional space and inevitably corrupted by noise and even outliers, which poses challenges for fully exploiting the intrinsic structure of views; (2) the non-convex objective functions prone to becoming stuck into bad local minima; and (3) the high-order structure information has been largely ignored, resulting in suboptimal solution. To alleviate these problems, this paper proposes a novel method, namely Self-paced Latent Embedding Space Learning (SLESL). Specifically, the views are projected into a latent embedding space to dimensional-reduce and clean the data, from simplicity to complexity in a self-paced manner. Meanwhile, multiple candidate graphs are learned in the latent space by using embedded self-expressiveness learning. After that, these graphs are stacked into a tensor to exploit the high-order structure information of views, such that a refined consensus affinity graph can be obtained for spectral clustering. The experimental results demonstrate the effectiveness of our proposed method.