Learn Before
Coordinate Ascent Perspective of the Expectation-Maximization Algorithm
The Expectation-Maximization (EM) algorithm can be interpreted as a coordinate ascent algorithm. By treating both the latent variables and the model parameters as variables to be optimized, the EM algorithm alternates between optimizing one set of variables while holding the other fixed. Consequently, each optimization step (the E-step and the M-step) is parallel to one of the coordinate axes in the parameter space.
0
3
Contributors are:
Who are from:
Tags
Data Science
Related
Application Scenarios of Using EM Algorithm
The relationship between EM algorithm with Jensen's Inequality
The Coin Example of EM Algorithm
The student example of EM algorithm
Convergence of EM Algorithm
A Helpful Presentation Explaining mathematical Details and Applications of EM Algorithm Provided by Berkeley
E Step
M step
Coordinate Ascent Perspective of the Expectation-Maximization Algorithm
Difficulty of Maximum Likelihood Estimation with Latent Variables