Existing methods include Kronecker compressed sensing (KCS) for sparse tensors and multi-way compressed sensing (MWCS) for sparse and low-rank tensors. KCS ...
Apr 5, 2014 · In this chapter, we introduce Generalized Tensor Compressed Sensing (GTCS)--a unified framework for compressed sensing of higher-order tensors.
In this paper, we propose generalized tensor compressive sensing (GTCS)-a unified framework for CS of higher order tensors, which preserves the intrinsic ...
May 24, 2013 · In this paper, we propose Generalized Tensor Compressive Sensing (GTCS)--a unified framework for compressive sensing of higher-order tensors.
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Abstract—For linear models, compressed sensing theory and methods enable recovery of sparse signals of interest from few measurements.
Two Algorithms for Compressed Sensing of Sparse Tensors. Shmuel Friedland, Qun Li, Dan Schonfeld, Edgar A. Bernal. Pages 259-281. Download chapter PDF · Sparse ...
In particular, we use two parallel sets of group tests, one to filter and the other to certify and estimate; the resulting algorithms are quite simple to.
The proofs naturally suggest a two-step recovery process: fitting a low-rank model in compressed domain, followed by per-mode decompression. This two-step ...
Over the recent years, a new approach for obtaining a succinct approximate representation of n- dimensional vectors (or signals) has been discovered.
Compressed sensing requires that undersampling causes incoherent artifacts so pseudorandom sampling schemes or radial/spiral sampling schemes are often used.