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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...
The theory of compressed sensing shows that sparse signals in high-dimensional spaces can be recovered from a relatively small number of samples in the form of random projections. However, in severely resource-constrained settings even CS techniques may fail, and thus, a less aggressive goal of partial signal recovery is reasonable. This paper describes a simple data-adaptive procedure that efficiently...
A multi-step adaptive resampling procedure is proposed, and shown to be an effective approach when detecting high-dimensional sparse signals in noise. Each step of the proposed procedure refines an estimate of the true signal subspace, allowing sensing energy to be focused more directly into the subspace of interest and significantly improving the performance of the final detection test. Large-sample...
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