Abstract
Subspace clustering is a popular method for clustering unlabelled data. However, the computational cost of the subspace clustering algorithm can be unaffordable when dealing with a large data set. Using a set of dimension sketched data instead of the original data set can be helpful for mitigating the computational burden. Thus, finding a way for dimension sketching becomes an important problem. In this paper, a new dimension sketching algorithm is proposed, which aims to select informative dimensions that have significant effects on the clustering results. Experimental results reveal that this method can significantly improve subspace clustering performance on both synthetic and real-world datasets, in comparison with two baseline methods.