Piecewise Toeplitz matrices-based sensing for rank minimization
2014 (English)In: European Signal Processing Conference, 2014, 1836-1840 p.Conference paper (Refereed)
This paper proposes a set of piecewise Toeplitz matrices as the linear mapping/sensing operator A: Rn1×n2 → RM for recovering low rank matrices from few measurements. We prove that such operators efficiently encode the information so there exists a unique reconstruction matrix under mild assumptions. This work provides a significant extension of the compressed sensing and rank minimization theory, and it achieves a tradeoff between reducing the memory required for storing the sampling operator from O(n1n2M) to O(max(n1, n2)M) but at the expense of increasing the number of measurements by r. Simulation results show that the proposed operator can recover low rank matrices efficiently with a reconstruction performance close to the cases of using random unstructured operators.
Place, publisher, year, edition, pages
2014. 1836-1840 p.
Coherence, compressed sensing, Rank minimization, Toeplitz matrix, Coherent light, Signal processing, Signal reconstruction, Linear mapping, Low-rank matrices, Piece-wise, Rank minimizations, Reconstruction matrix, Toeplitz matrices, Matrix algebra
Electrical Engineering, Electronic Engineering, Information Engineering
IdentifiersURN: urn:nbn:se:kth:diva-167937ScopusID: 2-s2.0-84911887113ISBN: 9780992862619OAI: oai:DiVA.org:kth-167937DiVA: diva2:817557
22nd European Signal Processing Conference, EUSIPCO 2014, 1 September 2014 through 5 September 2014
QC 201506052015-06-052015-05-222015-06-05Bibliographically approved