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  • Lecture Note
  • Gaussian Mixture Model
  • Example: Object segmentation
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  • Reference

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  1. Machine Learning
  2. ML Notes
  3. Parameter, Density Estimation

Gaussian Mixture Model

PreviousMAP, MLENextE-M

Last updated 3 years ago

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Lecture Note

Gaussian Mixture Model

: TAMU

: VTech

Gaussian Distribution

The density can be estimated by multiples of Gaussian Kernels

The mixing coefficients are themselves probabilities and must meet this condition: sum(pi)=1

Lets use the Maximum Likelihood Estimation to find the optimal parameter. This can be solved by E-M algorithm/

Example: Object segmentation

E-M algorithm for Gaussian Mixture Kernel

Reference

머신러닝/패턴인식, 오일석

π\piπis prior, not likelihood.

How to find the optimal parameter Θ\ThetaΘ?

see : VTech

The Hundred-Page Machine Learning Book

PPT slides
Read here for detail
http://themlbook.com/wiki/doku.php
GMM-EM class note
Click here for PPT slides
Click here for PPT slides
https://towardsdatascience.com/gaussian-mixture-models-explained-6986aaf5a95towardsdatascience.com