gmm_diag and gmm_full: C++ classes for multi-threaded Gaussian mixture models and Expectation-Maximisation
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Updated
Jul 4, 2022 - C++
gmm_diag and gmm_full: C++ classes for multi-threaded Gaussian mixture models and Expectation-Maximisation
A class for unsupervised classification using Expectation Maximization
"Octopus Realtime Encephalography Lab" is the (hard) real-time networked EEG-lab framework I have developed during my PhD Thesis at Brain Research Lab of Hacettepe University Faculty of Medicine Biophysics Lab. It is meant to be a holistic golden-standard solution for all tasks of cortical source localization/networking, brain-computer interface…
Parallelized implementation of K++, optimized and unoptimized versions of Lloyd's algorithm, and light weight coresets for K-Means clustering. All methods support serial, multi-threaded, distributed and hybrid levels of parallelism. The distance function is also interchangeable.
Clustering of image by k-means algorithm
First assignment for the University Senior Project course
Here we use K-Means and LBG algorithms to find an optimal codebook.
This project consists in the implementation of the K-Means and Mini-Batch K-Means clustering algorithms. This is not to be considered as the final and most efficient algorithm implementation as the objective here is to make a clear comparison between the sequential and parallel execution of the clustering steps.
K-Means Algorithm implemented using sequential and parallel algorithms.
This program implements a simple unsupervised classification scheme, K-means clustering, to classify PPM images into different categories/types.
Implementation of paraller k-means clustering in MPI
Generating image palette using K-Means Clustering
Implementation and survey of similarity search methods that rely on dimensionality reduction (e.g. LSH), D-dimensional vector clustering
Unsupervised Learning Classification with K-Means
Implementation of K Means Clustering Algorithm in C++ and Java to run on csv dataset files.
📐 Geometry Uni Assignments
K-Means image segmentation (feature extraction) that just works. Lightweight and low footprint C++ implementation.
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