Projection-Based QLP Algorithm for Efficiently Computing Low-Rank Approximation of Matrices

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.

Automated summary

A new algorithm called PbP-QLP is introduced in this paper for efficiently approximating low-rank matrices without using pivoting strategy, which allows it to leverage modern computer architectures better than competing randomized algorithms. The efficiency and effectiveness of PbP-QLP are demonstrated through various classes of synthetic and real-world data matrices.