Bayesian MCMC matrix factorization algorithm
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Updated
May 29, 2024 - C++
Bayesian MCMC matrix factorization algorithm
R package implementing Bayesian NMF using various models and prior structures.
Detecting mutational signatures via bayesian inference and a reference catalog
Non-Negative Matrix Tri-Factorization for Co-clustering
Python package for integrating and analyzing multiple single-cell datasets (A Python version of LIGER)
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.
MLForce stands for Machine Learning Force, which is a Python toolkit for machine learning beginners.
Non-Negative Matrix Factorization for Binary Data
NumPyNMF implements nine different Non-negative Matrix Factorization (NMF) algorithms using NumPy library and compares the robustness of each algorithm to five various types of noise in real-world data applications.
Repo for the article "Dynamics of White Matter Architecture in Lexical Production among Middle-Aged Adults"
My assignments for homework of Computational Data Mining course at Amirkabir University of Technology
Non-negative Matrix Factorisation for Stata.
Identifying conserved functional modules in multiple biological networks
UW / FDA
OCTIS: Comparing Topic Models is Simple! A python package to optimize and evaluate topic models (accepted at EACL2021 demo track)
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".
Codes and data coming with article "A Survey and an Extensive Evaluation of Popular Audio Declipping Methods", and others closely related
A comparative analysis between 4 topic modeling methods: LDA, NMF, BTM and CoreEx
Machine learning homework exploring image analysis and PCA dimensionality reduction.
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