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Fast and efficient dimensionality reduction using Structurally Random Matrices
2009
2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Structurally Random Matrices (SRM) are first proposed in [1] as fast and highly efficient measurement operators for large scale compressed sensing applications. Motivated by the bridge between compressed sensing and the Johnson-Lindenstrauss lemma [2] , this paper introduces a related application of SRMs regarding to realizing a fast and highly efficient embedding. In particular, it shows that a SRM is also a promising dimensionality reduction transform that preserves all pairwise distances of
doi:10.1109/icassp.2009.4959960
dblp:conf/icassp/DoGCNT09
fatcat:aqkizzcefzfufi7qhrq6mfg2dy