l_1-spectral clustering algorithm: a robust spectral clustering using Lasso regularization
Detecting cluster structure is a fundamental task to understand and visualize functional characteristics of a graph. Among the different clustering methods available, spectral clustering is one of the most widely used due to its speed and simplicity, while still being sensitive to high perturbations imposed on the graph. In this work, we present a robust variant of spectral clustering, called l_1-spectral clustering, based on Lasso regularization and adapted to perturbed graph models. By promoting sparse eigenbases solutions of specific l_1-minimization problems, it detects the hidden natural cluster structure of the graph. The effectiveness and robustness to noise perturbations is confirmed through a collection of simulated and real biological data.