Sensor selection in nonparametric decentralized detection is investigated. Kernel-based minimization framework with a weighted kernel is adopted, where the kernel weight parameters represent sensors' contributions to decision making. L1 regularization on weight parameters is introduced into the risk function so that the resulting optimal decision rule contains a sparse vector of nonzero weight parameters. In this way, sensor selection is naturally performed because only sensors corresponding to nonzero weight parameters contribute to decision making. A gradient projection algorithm and a Gauss-Seidel algorithm are developed to jointly perform weight selection (i.e., sensor selection) and optimize decision rules. Both algorithms are shown to converge to critical points for this non-convex optimization problem. Numerical results are provided to demonstrate the advantages and properties of the proposed sensor selection approach.