Skip to content

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

Notifications You must be signed in to change notification settings

catherman/Image-Analysis-with-Gaussian-Mixture-Model

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

22 Commits
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Image-Analysis-with-Gaussian-Mixture-Model

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.

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

No packages published

Languages