Robust PCA in Python. Methods are from the http://perception.csl.illinois.edu/matrix-rank/sample_code.html and papers therein.

• scipy
• numpy
• pypropack(optional)
• scikit-learn
• nosetest

• test_robustpca.py test whether the algorithms included can recovery the synthetic data successfully. Use nosetest test_robustpca.py
• plot_benchmark.py plot the benchmarks with synthetic data generated with different parameters. Use python2 plot_benchmark.py
• background_subtraction.py generate the result using the escalator dataset. Use python2 background_subtraction.py. This will generate the .mat files with respect to each algorithms and can be directly readable from matlab. Furthermore, background_subtraction_visualize.py could be used to generate a video. The temporary image files are located in /tmp/robust_pca_tmp/ which should be created first.
• topic_extraction.py extracts the keywords from the 20newsgroup dataset. It will generate two files, one is origin.txt and another is keyword.txt. The keyword and the original text on the same line is one-one mapped.

Special thanks for the following two resources and their authors.

• http://perception.csl.illinois.edu/matrix-rank/sample_code.html
• www.stanford.edu/~peleato/math301_slides.pdf‎