MS Yang, A robust EM clustering algorithm for Gaussian mixture models, Pattern Recognit., 45 (2012), pp. 3950-3961
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Updated
Jun 25, 2021 - Python
MS Yang, A robust EM clustering algorithm for Gaussian mixture models, Pattern Recognit., 45 (2012), pp. 3950-3961
This repository is for sharing the scripts of EM algorithm and variational bayes.
ModelGaussian_Mixture_Model
Model-based clustering based on parameterized finite Gaussian mixture models. Models are estimated by EM algorithm initialized by hierarchical model-based agglomerative clustering. The optimal model is then selected according to BIC.
Code for the paper Data-efficient model learning and prediction for contact-rich manipulation tasks, RA-L, 2020
Implementation of Task-Parameterized-Gaussian-Mixture-Models as presented from S. Calinon in his paper: "A Tutorial on Task-Parameterized Movement Learning and Retrieval"
We are given 2 different problems to solve. 1. Isolated spoken digit recognition 2. Telugu Handwritten character recognition Both these datasets were given as a time series. 2 different methods were used to solve each of the problem: 1. Dynamic Time Warping 2. Hidden Markov Models
Clustering algorithm implementaions from scratch with python (k-means, EM-GMM, mean-shift, agglomerative)
Expectation-Maximization (EM) algorithm for Gaussian mixture model (GMM) from scratch
The project encompasses the statistical analysis of data using different clustering and feature selection techniques.
A UI for Sprocket-VC
Underwater Buoy detection using Gaussian Mixture Models (GMM) and Expectation-Maximization (EM) Algorithm
Image analysis with Gaussian Mixture Model (GMM), with Principal Component Analysis (PCA) for dimensionality reduction of images prior to expectation-maximization (EM) algorithm implementation.
Image Clustering using PCA and GMM: A project implementing Principal Component Analysis and Gaussian Mixture Models for efficient image clustering.
Gaussian Mixture Model with low rank approximation
In a given dataset we run a Gaussian Mixture Model clustering and then analyze that clustering with K means
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