Python package for point cloud registration using probabilistic model (Coherent Point Drift, GMMReg, SVR, GMMTree, FilterReg, Bayesian CPD)

Python machine learning applications in image processing, recommender system, matrix completion, netflix problem and algorithm implementations including Co-clustering, Funk SVD, SVD++, Non-negative Matrix Factorization, Koren Neighborhood Model, Koren Integrated Model, Dawid-Skene, Platt-Burges, Expectation Maximization, Factor Analysis, ISTA, F…

Python implementation of EM algorithm for GMM. And visualization for 2D case.

AI Learning Hub for Machine Learning, Deep Learning, Computer Vision and Statistics

Implementing Gaussian Mixture Model from scratch using python class and Expectation Maximization algorithm. It is a clustering algorithm having certain advantages over kmeans algorithm.

Learning Bayesian Network parameters using Expectation-Maximisation

Python implementation of a complex-valued version of the expectation-maximization (EM) algorithm for fitting Gaussian Mixture Models (GMMs).

Infers species direct association networks

mfair: Matrix Factorization with Auxiliary Information in R

Streamflow reconstruction using linear dynamical system

Machine Learning From Scratch

A class for unsupervised classification using Expectation Maximization

State space model + data pipeline to generate counterfactual time series trajectories on multiple clinical signals, used to evaluate the utility of counterfactual features in sepsis prediction

C++ Implementation of EMASE

The project development aims to interpolate the seawater temperature, salinity three-dimensional structure, and to understand the physical oceanography in the turbulent region Gulf Stream by exploiting the latent regression model and deep regression neural networks.

SJTU CS420

Non-homogenous Hidden Markov Models

Explanate how the algorithm of Expectation-Maximization in Gaussian Mixture works for clustering.