wtf: Wasserstein Tensor Factorisation

A unified framework for non-negative matrix and tensor factorisations with a smoothed Wasserstein loss

This repository contains a basic implementation of the method described in the article "A unified framework for non-negative matrix and tensor factorisations with a smoothed Wasserstein loss".

Requirements

PyTorch
Tensorly
PythonOT
CUDA-compatible GPU (e.g. use Colab) for efficient autodiff

Instructions

Clone this repo: git clone https://github.com/zsteve/wtf.git

Import the wtf module using

import sys
sys.path.insert(0, "/content/wtf/src")
import wtf

???
Profit

Example

From the paper

A notebook for Figures 3, 4, 5 is located in the examples/ directory.

Amino acids fluorescence dataset

A notebook for the amino acids fluorescence dataset of Andersson and Bro is available as examples/amino.ipynb.

Thanks to Shoaib Bin Masud (Tufts) and Anna Konstorum (Yale) for bringing this dataset to my attention.

Citing

If you find this work relevant to your research project, please cite the preprint

Zhang, S. A unified framework for non-negative matrix and tensor factorisations with a smoothed Wasserstein loss, arXiv preprint arXiv:2104.01708, 2021