Online Identification and Tracking of Subspaces from Highly Incomplete Information

Details Online Identification and Tracking of Subspaces from Highly Incomplete Information Online Identification and Tracking of Subspaces from Highly Incomplete Information

2010-06-21

arxiv.org/abs/1006.4046v2

Computer Science

Information Theory

Scientific paper

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.

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