  Matrices with low numerical rank are omnipresent in many signal processing
and data analysis applications. The pivoted QLP (p-QLP) algorithm constructs a
highly accurate approximation to an input low-rank matrix. However, it is
computationally prohibitive for large matrices. In this paper, we introduce a
new algorithm termed Projection-based Partial QLP (PbP-QLP) that efficiently
approximates the p-QLP with high accuracy. Fundamental in our work is the
exploitation of randomization and in contrast to the p-QLP, PbP-QLP does not
use the pivoting strategy. As such, PbP-QLP can harness modern computer
architectures, even better than competing randomized algorithms. The efficiency
and effectiveness of our proposed PbP-QLP algorithm are investigated through
various classes of synthetic and real-world data matrices.
