Norton Rose Fulbright US LLP
Systems and methods for robust low-rank matrix approximation
Systems and methods which provide robust low-rank matrix approximation using low-rank matrix factorization in the lp-norm space, where p<2 (e.g., 1≤p<2), providing a lp-PCA technique are described. For example, embodiments are configured to provide robust low-rank matrix approximation using low-rank matrix factorization in the least absolute deviation (l1-norm) space providing a l1-PCA technique. Embodiments minimize the lp-norm of the residual matrix in the subspace factorization of an observed data matrix, such as to minimize the l1-norm of the residual matrix where p=1. The alternating direction method of multipliers (ADMM) is applied according to embodiments to solve the subspace decomposition of the low-rank matrix factorization with respect to the observed data matrix. Iterations of the ADMM may comprise solving a l2-subspace decomposition and calculating the proximity operator of the l1-norm.
City University of Hong Kong
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Hing Cheung Wen-Jun Jiayi
So Zeng Chen