Tensor Laplacian Regularized Low-Rank Representation for Non-Uniformly Distributed Data Subspace Clustering

Low-Rank Representation (LRR) suffers from discarding the locality information of data points in subspace clustering. We propose a hypergraph-based model by incorporation of a variable number of adjacent nodes, locality information, and sparsity of subspaces. An optimization problem is defined and solved by developing a tensor Laplacian-based algorithm.The outperformance of the proposed method is substantial when the inherent structure of the data exploits severe nonlinearity, geometrical overlapping, and outliers.