OCTIS: Comparing Topic Models is Simple! A python package to optimize and evaluate topic models (accepted at EACL2021 demo track)

Non-negative Matrix Factorization (NMF) Tensorflow Implementation

Bayesian MCMC matrix factorization algorithm

Python PyTorch (GPU) and NumPy (CPU)-based port of Févotte and Dobigeon's robust-NMF algorithm appearing in "Nonlinear hyperspectral unmixing with robust nonnegative matrix factorization."

Codes and data coming with article "A Survey and an Extensive Evaluation of Popular Audio Declipping Methods", and others closely related

An algorithm for unsupervised discovery of sequential structure

Built a collaborative filtering and content-based recommendation/recommender system specific to H&M using the Surprise library and cosine similarity to generate similarity and distance-based recommendations.

Python package for integrating and analyzing multiple single-cell datasets (A Python version of LIGER)

✨ Awesome - A curated list of amazing Topic Models (implementations, libraries, and resources)

Python library for phase identification and spectrum analysis of energy dispersive x-ray spectroscopy (EDS)

PyTorch implementation of Robust Non-negative Tensor Factorization appearing in N. Dey, et al., "Robust Non-negative Tensor Factorization, Diffeomorphic Motion Correction and Functional Statistics to Understand Fixation in Fluorescence Microscopy".

New Matrix Factorization Algorithms based on Bregman Proximal Gradient: BPG-MF, CoCaIn BPG-MF, BPG-MF-WB

Topic modeling streamlit app.

Optimization and Regularization variants of Non-negative Matrix Factorization (NMF)

A C++ framework of Distributed Non-Negative Matrix Factorization implementation to find Latent Dimensionality in Big Data

A blind source separation package using non-negative matrix factorization and non-negative ICA

Projects for ECE 475 - Freq. Machine Learning

The project develops an application that suggests to the reader more similar articles to that he already read. It uses the embedding algorithms of headlines to create their own numerical representation, which allows to compute the similarity between headlines and get the most similar ones.

Context-aware non negative matrix factorization clustering. ICPR2016