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CORR
2010
Springer

Online Identification and Tracking of Subspaces from Highly Incomplete Information

10 years 2 months ago
Online Identification and Tracking of Subspaces from Highly Incomplete Information
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.
Laura Balzano, Robert Nowak, Benjamin Recht
Added 09 Dec 2010
Updated 09 Dec 2010
Type Journal
Year 2010
Where CORR
Authors Laura Balzano, Robert Nowak, Benjamin Recht
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