A Rank-Constrained Clustering Algorithm with Adaptive Embedding

Most spectral clustering algorithms obtain firstly the embedding based on the similarity matrix of the input data and then discretize the embedding to get the final clustering result. So, their performance is greatly affected by the noise of the similarity matrix, and the two-step strategy may lead to a suboptimal result. In this article, a novel Rank-Constrained clustering algorithm with Adaptive Embedding called RCAE is proposed, where the spectral embedding and the clustering structure are learned simultaneously, so, the influence of noise on performance is greatly reduced. In addition, a rank constraint is adopted in our model, thus, the connectivity matrix with exactly c (the number of clusters to construct) connected components can be learned, therefore, the final clustering result can be obtained according to the connected components. Experiments on several benchmark datasets validate the superiority of the proposed methods, compared to the several state-of-the-art clustering algorithms [GitHub].