Personalized PCA

This is the implementation of the paper Personalized PCA: decoupling shared and unique features.

If you run into any problems, please submit an issue, or contact the authors of that paper.

How to test a simple example

run python3 ppca.py --dataset=borrowpowertest --logoutput=True

Results will be generated to the "outputs/" folder

How to run the video segmentation experiment

Create a folder called "images/"
Put all the image frames of the video in the "images/" folder
Rewrite the first line of the function load_car_data in the file imgpro.py accordingly
Run python3 ppca.py --dataset='img_test' --logoutput=True
If needed, fine-tune 'ngc' and 'nlc' to get the best results

How to run the presidential debate topics modeling experiment

Download the dataset from this link
Put all files in the "debate/" folder
Run python3 ppca.py --dataset='debate_test' --logoutput=True

File organization

ppca.py contains all the hyperparameters
algs.py contains the implementation of personalized PCA learning algorithm
imgpro.py, vectorize.py, mnist.py are used to handle video data, debate corpus, and FEMNIST dataset respectively.