https://github.com/dganguli/robust-pca

A simple Python implementation of R-PCA
https://github.com/dganguli/robust-pca

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A simple Python implementation of R-PCA

• Host: GitHub
• URL: https://github.com/dganguli/robust-pca
• Owner: dganguli
• License: mit
• Created: 2014-03-03T23:36:27.000Z (over 10 years ago)
• Default Branch: master
• Last Pushed: 2023-05-19T22:03:55.000Z (over 1 year ago)
• Last Synced: 2024-10-18T06:57:46.127Z (27 days ago)
• Language: Python
• Homepage:
• Size: 9.77 KB
• Stars: 226
• Watchers: 13
• Forks: 66
• Open Issues: 4
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
Robust-PCA

==========

A Python implementation of R-PCA using principle component pursuit by alternating directions. The theory and implementation of the algorithm is described here: https://arxiv.org/pdf/0912.3599.pdf (doi > 10.1145/1970392.1970395)

```python

# generate low rank synthetic data

N = 100

num_groups = 3

num_values_per_group = 40

p_missing = 0.2

Ds = []

for k in range(num_groups):

    d = np.ones((N, num_values_per_group)) * (k + 1) * 10

    Ds.append(d)

D = np.hstack(Ds)

# decimate 20% of data 

n1, n2 = D.shape

S = np.random.rand(n1, n2)

D[S < 0.2] = 0

# use R_pca to estimate the degraded data as L + S, where L is low rank, and S is sparse

rpca = R_pca(D)

L, S = rpca.fit(max_iter=10000, iter_print=100)

# visually inspect results (requires matplotlib)

rpca.plot_fit()

plt.show()

```