Delineation of large-scale functional networks (FNs) from resting state functional MRI data has become a standard tool to explore the functional brain organization in neuroscience. However, existing methods sacrifice subject specific variation in order to maintain the across-subject correspondence necessary for group-level analyses. In order to obtain subject specific FNs that are comparable across subjects, existing brain decomposition techniques typically adopt heuristic strategies or assume a specific statistical distribution for the FNs across subjects, and therefore might yield biased results. Here we present a novel data-driven method for detecting subject specific FNs while establishing group level correspondence. Our method simultaneously computes subject specific FNs for a group of subjects regularized by group sparsity, to generate subject specific FNs that are spatially sparse and share common spatial patterns across subjects. Our method is built upon non-negative matrix decomposition techniques, enhanced by a data locality regularization term that makes the decomposition robust to imaging noise and improves spatial smoothness and functional coherences of the subject specific FNs. Our method also adopts automatic relevance determination techniques to eliminate redundant FNs in order to generate a compact set of informative sparse FNs. We have validated our method based on simulated, task fMRI, and resting state fMRI datasets. The experimental results have demonstrated our method could obtain subject specific, sparse, non-negative FNs with improved functional coherence, providing enhanced ability for characterizing the functional brain of individual subjects.