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This project focuses on low-rank matrix restoration with robust principal component analysis (RPCA) and matrix completion (MC).

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jingxuanyang/LowRankMatrixRestoration

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Low-Rank Matrix Restoration

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Introduction

This project focuses on low-rank matrix restoration with robust principal component analysis (RPCA) and matrix completion (MC). We have implemented the following algorithms.

Structure

The directory of this project is listed as follows.

|- bin/
|- data/
|- doc/
|- ntk/
|- pre/
|- utils/
|- README.md
|- requirements.txt

File Descriptions

  • bin/
    • This folder contains the executable code files
  • data/
    • This folder contains images and results
  • doc/
    • This folder contains the report of this project written with LaTeX
  • ntk/
    • This folder is taken from [link]
  • pre/
    • This folder contains the presentation slides.
  • utils/
    • This folder contains utilities of this project
  • README.md
    • This file serves as user manual of this project
  • requirements.txt
    • This file contains python packages used in this project

Quick Start

  1. Please install required packages included in requirements.txt via pip.
~$ pip install -r requirements.txt
  1. Kindly run bin/low_rank_re.py to test all algorithms on images in data/.
~$ cd bin
~bin$ python low_rank_re.py
  1. Please run bin/results_analysis.ipynb to analyze the results and plot figures.

Results

PCA

Robust PCA

Matrix Completion

References

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This project focuses on low-rank matrix restoration with robust principal component analysis (RPCA) and matrix completion (MC).

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