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

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

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."

This repository provides Python implementations for Non-negative Matrix Factorization (NMF) using the Multiplicative Update (MU) algorithm. Two initialization methods are supported: random initialization and Non-negative Double Singular Value Decomposition (NNDSVD). NMF is a matrix factorization technique used in various fields, including topic mod

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

Non-negative Matrix Factorization (NMF) Tensorflow Implementation

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

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

An algorithm for unsupervised discovery of sequential structure

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".

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

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

Topic modeling streamlit app.

Coupled clustering of single cell genomic data

Analysis of the robustness of non-negative matrix factorization (NMF) techniques: L2-norm, L1-norm, and L2,1-norm

PyTorch implementations of the beta divergence loss.

Tensor Extraction of Latent Features (T-ELF). Within T-ELF's arsenal are non-negative matrix and tensor factorization solutions, equipped with automatic model determination (also known as the estimation of latent factors - rank) for accurate data modeling. Our software suite encompasses cutting-edge data pre-processing and post-processing modules.

An official implementation of "Joint Inference of Diffusion and Structure in Partially Observed Social Networks Using Coupled Matrix Factorization"