fastRPCA
========
Matlab code for all variants of robust PCA and SPCP. This implements the code from the conference paper "A variational approach to stable principal component pursuit" by Aravkin, Becker, Cevher, Olsen; UAI 2014.
Not only is this code fast, but it is the only code we know of that solves all common stable principal component pursuit (SPCP) variants, including the new variants we introduced in the paper. All these variants are in some sense equivalent, but some of them are easier to solve, and some have parameters that are easier to estimate. See the [paper for details](http://arxiv.org/abs/1406.1089)
<p align="center"><img src="http://amath.colorado.edu/faculty/becker/escalatorImage.jpg" /></p>
More info on robust PCA and stable principal component pursuit
(websites with software, review articles, etc.)
* ["A variational approach to stable principal component pursuit"](http://arxiv.org/abs/1406.1089)
* [LRS Library](https://github.com/andrewssobral/lrslibrary)
* [Matrix Factorization jungle](https://sites.google.com/site/igorcarron2/matrixfactorizations)
* [One of the first papers on RPCA](http://arxiv.org/abs/0912.3599)
Citation
---------
bibtex:
```
@inproceedings{aravkin2014,
author = "Aravkin, A. and Becker, S. and Cevher, V. and Olsen, P.",
title = "A variational approach to stable principal component pursuit",
booktitle = "Conference on Uncertainty in Artificial Intelligence (UAI)",
year = "2014",
month = "July",
}
```
Code and installation
----
The code runs on MATLAB and does not require any mex files or installation. Just unzip the file and you are set. Run `setup_fastRPCA` to set the correct paths, and try the `demos` directory for sample usage of the code.
[![View fastRPCA on File Exchange](https://www.mathworks.com/matlabcentral/images/matlab-file-exchange.svg)](https://www.mathworks.com/matlabcentral/fileexchange/52359-fastrpca)
Authors
--------------
The code was developed by all the authors of the UAI paper, but primary development is due to Stephen Becker and Aleksandr (Sasha) Aravkin (when both were at IBM Research). Further contributions are welcome.
## Contact us
* [Sasha Aravkin](https://uw-amo.github.io/saravkin/), University of Washington, Seattle
* [Stephen Becker](amath.colorado.edu/faculty/becker/), University of Colorado Boulder
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毕业设计&课设-用于鲁棒PCA和SPCP的所有变体的Matlab代码.zip (27个子文件)
fastRPCA-master
.DS_Store 6KB
solvers
solver_RPCA_constrained.m 29KB
solver_split_SPCP.m 2KB
solver_RPCA_Lagrangian.m 3KB
solver_RPCA_SPGL1.m 8KB
VERSION 19B
utilities
.DS_Store 6KB
lbfgs_gpu.m 4KB
WolfeLineSearch.m 4KB
referenceSolutions
downloadEscalatorData.m 442B
verySmall_l1l2.mat 3KB
downloadReferenceSolutions.m 211B
escalator_data.mat 3.61MB
verySmall_l1.mat 3KB
randomizedSVD.m 4KB
vec.m 37B
lbfgsAdd.m 581B
lbfgsProd.m 861B
errorFunction.m 2KB
func_split_spcp.m 2KB
loadSyntheticProblem.m 14KB
holdMarker.m 3KB
demos
demo_escalatorVideo.m 2KB
test_l1l2.m 5KB
recreatePaperExperiment.m 11KB
setup_fastRPCA.m 163B
README.md 2KB
共 27 条
- 1
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