In this analysis, we first use Principal Component Analysis (PCA) to reduce the dimensionality of the images. We then implement the expectation-maximization (EM) algorithm to fit a Gaussian mixture model (GMM) with the MNIST handwritten digits dataset. These results are compared to analysis using k-means clustering.
-
Notifications
You must be signed in to change notification settings - Fork 0
Image analysis with Gaussian Mixture Model (GMM), with Principal Component Analysis (PCA) for dimensionality reduction of images prior to expectation-maximization (EM) algorithm implementation.
License
catherman/Image-Analysis-with-Gaussian-Mixture-Model
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
Folders and files
| Name | Name | Last commit message | Last commit date | |
|---|---|---|---|---|
Repository files navigation
About
Image analysis with Gaussian Mixture Model (GMM), with Principal Component Analysis (PCA) for dimensionality reduction of images prior to expectation-maximization (EM) algorithm implementation.
Topics
Resources
License
Stars
Watchers
Forks
Releases
No releases published
Packages 0
No packages published