pyrpca

Python implementations of RPCA

Usage

import numpy as np
import pyrpca

n = 50 
r = 2
np.random.seed(123)
base = 100 + np.cumsum(np.random.randn(n,r),axis=0)
scales = np.abs(np.random.randn(n,r))
L = np.dot(base,scales.T)
S = np.round(0.25 * np.random.randn(n,n))
M = L + S

L_hat,S_hat,niter = pyrpca.rpca_alm(M)
np.max(np.abs(S-S_hat))
np.max(np.abs(L-L_hat))

_,s,_ = np.linalg.svd(L,full_matrices=False)
print s[s>1e-11]

_,s_hat,_ = np.linalg.svd(L_hat,full_matrices=False)
print s_hat[s_hat>1e-11]

Requirements

• Numpy
• PyPROPACK (https://github.com/jakevdp/pypropack)

Authors

pyrpca was written by Alex Papanicolaou <alex.papanic@gmail.com>_.

Reference

Candès, E. J., Li, X., Ma, Y., & Wright, J. (2011). Robust principal component analysis?. Journal of the ACM (JACM), 58(3), 11. http://statweb.stanford.edu/~candes/papers/RobustPCA.pdf