Incomplete Multi-view Subspace Clustering Using Non-uniform Hyper-graph for High-Order Information
Abstract: Incomplete multi-view subspace clustering (IMSC) is intended to exploit the information of multiple incomplete views to partition data into their intrinsic subspaces. Existing methods try to exploit the high-order information of data to improve the clustering performance, many tools are used such as tensor factorization and hyper-Laplacian regularization. Compared to using complex mathematical tools to solves problems, why not considering to get considerable improvements through some simple ways? To address this issue, we propose an incomplete multi-view subspace clustering method using non-uniform hyper-graph (NUHG-IMSC) method which makes slightly change to the usual way of constructing uniform hyper-graph. We find a set of data points that have high similarity with the center point of each hyper-edge in high-dimensional space to be its neighbor samples, the cardinality of each hyper-edge is decided based on the distribution of the corresponding center point. This is a simple but effective way to utilize high-order information without bringing computational burden and extra parameters. Besides the advantage that the partial samples can be reconstructed more reasonably, our method also brings benefits to other parts in the whole framework of IMSC, such as learning the view-specific affinity matrices, the weight of each view, and the unified affinity matrix. Experimental results on three multi-view data sets indicate the effectiveness of the proposed method.
Loading