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Ding, C., Zhou, D., He, X. and Zha, H. (2006) R1-PCA: Rotational Invariant L1-Norm Principal Component Analysis for Robust Subspace Factorization. The 23rd International Conference on Machine Learning, 23, 281-288.
https://doi.org/10.1145/1143844.1143880

has been cited by the following article:

• TITLE: Continuous Iteratively Reweighted Least Squares Algorithm for Solving Linear Models by Convex Relaxation

  AUTHORS: Xian Luo, Wanzhou Ye

  KEYWORDS: Linear Models, Continuous Iteratively Reweighted Least Squares, Convex Relaxation, Principal Component Analysis

  JOURNAL NAME: Advances in Pure Mathematics, Vol.9 No.6, June 27, 2019

  ABSTRACT: In this paper, we present continuous iteratively reweighted least squares algorithm (CIRLS) for solving the linear models problem by convex relaxation, and prove the convergence of this algorithm. Under some conditions, we give an error bound for the algorithm. In addition, the numerical result shows the efficiency of the algorithm.