GROUSE is an incremental stochastic algorithm for subspace identification based on incomplete information. This talk discusses recent results on the local convergence behavior of GROUSE, showing an expected linear convergence rate. Stronger results are possible when the full data vector is available. We describe too an equivalence between GROUSE and an incremental method based on the singular value decomposition.