SE-QRCS selects k representative columns of a wide matrix by applying strong rank-revealing QR to a sparse sketch and to the small induced column set, with spectral bounds that shrink the dependence on n.
Lu factorization with panel rank revealing pivoting and its communication avoiding version.SIAM Journal on Matrix Analysis and Applications, 34(3):1401–1429, 2013
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Efficient QR-based Column Subset Selection through Randomized Sparse Embeddings
SE-QRCS selects k representative columns of a wide matrix by applying strong rank-revealing QR to a sparse sketch and to the small induced column set, with spectral bounds that shrink the dependence on n.