A divide-and-conquer framework for large-scale subspace clustering

Given data that lies in a union of low-dimensional subspaces, the problem of subspace clustering aims to learn — in an unsupervised manner — the membership of the data to their respective subspaces. State-of-the-art subspace clustering methods typically adopt a two-step procedure. In the first step, an affinity measure among data points is constructed… CONTINUE READING