Constructing L2-Graph For Subspace Learning and Segmentation

Construction of sparse similarity graph is a fundamental and key step of graph-oriented learning algorithms. In a similarity graph, the vertex denotes a data point and the connection weight between two points represents the similarity. Some recent works used L1-norm based sparse coefficients to build the graph for various applications, and achieved impressive results. Can we find other way to achieve sparsity with better performance and fewer limitations?