Robust principal component analysis (robust PCA, RPCA) is a modification of principal component analysis (PCA) which works well with respect to grossly corrupted observations. The package implements robust PCA in exact alternating Lagrangian multipliers (EALM) algorithm and inexact alternating Lagrangian multipliers (IALM) algorithm. The implementation refers the paper and its MATLAB implementation.

Use the package manager pip to install.

pip install git+https://github.com/2020leon/rpca.git@v1.1.0

import numpy as np import rpca.ealm import rpca.ialm RNG = np.random.default_rng() D = RNG.random((20, 20)) A0, E0 = rpca.ealm.fit(D) A1, E1 = rpca.ialm.fit(D)

Contributing is welcome!

MIT

• Robust principal component analysis - Wikipedia
• Z. Lin, M. Chen, and Y. Ma, "The Augmented Lagrange Multiplier Method for Exact Recovery of Corrupted Low-Rank Matrices," 2010. doi: https://doi.org/10.48550/arXiv.1009.5055.
• MATLAB packages