Randomized Quaternion QLP Decomposition for Low-Rank Approximation

The low-rank approximation of a quaternion matrix has attracted growing attention in many applications including color image processing and signal processing. In this paper, based on quaternion normal distribution random sampling, we propose a randomized quaternion QLP decomposition algorithm for computing a low-rank approximation to a quaternion data matrix. For the theoretical analysis, we first present convergence results of the quaternion QLP decomposition, which provides slightly tighter upper bounds than the existing ones for the real QLP decomposition. Then, for the randomized quaternion QLP decomposition, the matrix approximation error and the singular value approximation error analyses are also established to show the proposed randomized algorithm can track the singular values of the quaternion data matrix with high probability. Finally, we present some numerical examples to illustrate the effectiveness and reliablity of the proposed algorithm.

Automated summary

In this paper, a randomized quaternion QLP decomposition algorithm is proposed for computing a low-rank approximation to a quaternion data matrix, based on quaternion normal distribution random sampling. The convergence results and upper bounds of the proposed algorithm outperform existing methods for real QLP decomposition, and the algorithm can track the singular values of the quaternion data matrix with high probability.