Sparse subspace clustering via closure subgraph based on directed graph

Abstract

Sparse subspace clustering has attracted much attention in the fields of signal processing, image processing, computer vision, and pattern recognition. We propose an algorithm, sparse subspace clustering via closure subgraph (SSC-CG) based on directed graph, to accomplish subspace clustering without the number of subspaces as prior information. In SSC-CG, we use a directed graph to express the relations in data instead of an undirected graph like most previous methods. Through finding all strongly connected components with closure property, we discovery all subspaces in the given dataset. Based on expressive relations, we assign data to subspaces or treat them as noise data. Experiments demonstrate that SSC-CG has an exciting performance in most conditions.