This work presents GROUSE (Grassmanian Rank-One Update Subspace Estimation), an efficient online algorithm for tracking subspaces from highly incomplete observations. GROUSE requires only basic linear algebraic manipulations at each iteration, and each subspace update can be performed in linear time in the dimension of the subspace. The algorithm is derived by analyzing incremental gradient descent on the Grassmannian manifold of subspaces. With a slight modification, GROUSE can also be used as an online incremental algorithm for the matrix completion problem of imputing missing entries of a low-rank matrix. GROUSE performs exceptionally well in practice both in tracking subspaces and as an online algorithm for matrix completion.
The GROUSE method proposed in REF used incremental gradient descent on the Grassmannian manifold of subspaces to learn a low rank factorization from incomplete data online.
Online identification and tracking of subspaces from highly incomplete information
2010 48th Annual Allerton Conference on Communication, Control, and Computing (Allerton)
2010 48th Annual Allerton Conference on Communication, Control, and Computing (Allerton)
Computer Science Mathematics