In many applications, high-dimensional data points can be well represented by low-dimensional subspaces. To identify the subspaces, it is important to capture a global and local structure of the data which is achieved by imposing low-rank and sparseness constraints on the data representation matrix. In low-rank sparse subspace clustering (LRSSC), nuclear and l1 -norms are used to measure rank and sparsity. However, the use of nuclear and l1 -norms leads to an overpenalized problem and only approximates the original problem. In this paper, we propose two l0 quasi-norm-based regularizations. First, this paper presents regularization based on multivariate generalization of minimax-concave penalty (GMC-LRSSC), which contains the global minimizers of a l0 quasi-norm regularized objective. Afterward, we introduce the Schatten-0 ( S0 ) and l0 -regularized objective and approximate the proximal map of the joint solution using a proximal average method ( S0/l0 -LRSSC). The resulting nonconvex optimization problems are solved using an alternating direction method of multipliers with established convergence conditions of both algorithms. Results obtained on synthetic and four real-world datasets show the effectiveness of GMC-LRSSC and S0/l0 -LRSSC when compared to state-of-the-art methods.