Grouping Subspace Segmentation

This work studies the subspace segmentation problem. Given a set of data points which are drawn from a union of multiple subspaces. The goal of subspace segmentation is to cluster the data into the underlying subspaces from which the data are drawn from. The most recent works use spectral clustering based on the affinity matrix obtained by Sparse Subspace Clustering (SSC), Low-Rank Representation (LRR) and Least Squares Regression (LSR). They write each data point as a linear combination of all the data points, and use the representation coefficients to form the affinity matrix. LSR shows that it is more effective due to the grouping effect for data with high correlation. In this work, we propose the Grouping Subspace Segmentation (GSS) method by enhancing the grouping effect of correlated data points. The affinity graph is constructed to encode the local structure of data. Experiments on the real data sets show that the proposed method GSS is more effective than the state-of-the-art methods.