Expectation Maximization Algorithm

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# Expectation Maximization Algorithm - PowerPoint PPT Presentation

Expectation Maximization Algorithm. Rong Jin. A Mixture Model Problem. Apparently, the dataset consists of two modes How can we automatically identify the two modes?. Gaussian Mixture Model (GMM). Assume that the dataset is generated by two mixed Gaussian distributions Gaussian model 1:

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### Expectation Maximization Algorithm

Rong Jin

A Mixture Model Problem
• Apparently, the dataset consists of two modes
• How can we automatically identify the two modes?
Gaussian Mixture Model (GMM)
• Assume that the dataset is generated by two mixed Gaussian distributions
• Gaussian model 1:
• Gaussian model 2:
• If we know the memberships for each bin, estimating the two Gaussian models is easy.
• How to estimate the two Gaussian models without knowing the memberships of bins?
EM Algorithm for GMM
• Let memberships to be hidden variables
• EM algorithm for Gaussian mixture model
• Unknown memberships:
• Unknown Gaussian models:
• Learn these two sets of parameters iteratively
• Random assign the memberships to each bin
• Random assign the memberships to each bin
• Estimate the means and variance of each Gaussian model
E-step
• Fixed the two Gaussian models
• Estimate the posterior for each data point
EM Algorithm for GMM
• Re-estimate the memberships for each bin

Weighted by posteriors

Weighted by posteriors

M-Step
• Fixed the memberships
• Re-estimate the two model Gaussian
EM Algorithm for GMM
• Re-estimate the memberships for each bin
• Re-estimate the models
At the 5-th Iteration
• Red Gaussian component slowly shifts toward the left end of the x axis
At the10-th Iteration
• Red Gaussian component still slowly shifts toward the left end of the x axis
At the 20-th Iteration
• Red Gaussian component make more noticeable shift toward the left end of the x axis
At the 50-th Iteration
• Red Gaussian component is close to the desirable location
At the 100-th Iteration
• The results are almost identical to the ones for the 50-th iteration
EM as A Bound Optimization
• EM algorithm in fact maximizes the log-likelihood function of training data
• Likelihood for a data point x
• Log-likelihood of training data
EM as A Bound Optimization
• EM algorithm in fact maximizes the log-likelihood function of training data
• Likelihood for a data point x
• Log-likelihood of training data
EM as A Bound Optimization
• EM algorithm in fact maximizes the log-likelihood function of training data
• Likelihood for a data point x
• Log-likelihood of training data
Logarithm Bound Algorithm
Logarithm Bound Algorithm

Touch Point

• Come up with a lower bounded
Logarithm Bound Algorithm
• Come up with a lower bounded
• Search the optimal solution that maximizes
Logarithm Bound Algorithm
• Come up with a lower bounded
• Search the optimal solution that maximizes
• Repeat the procedure
Logarithm Bound Algorithm

Optimal Point

• Come up with a lower bounded
• Search the optimal solution that maximizes
• Repeat the procedure
• Converge to the local optimal
EM as A Bound Optimization
• Parameter for previous iteration:
• Parameter for current iteration:
• Compute
Maximize GMM Model
• What is the global optimal solution to GMM?
• Maximizing the objective function of GMM is ill-posed problem
Maximize GMM Model
• What is the global optimal solution to GMM?
• Maximizing the objective function of GMM is ill-posed problem
Identify Hidden Variables
• For certain learning problems, identifying hidden variables is not a easy task
• Consider a simple translation model
• For a pair of English and Chinese sentences:
• A simple translation model is
• The log-likelihood of training corpus
Identify Hidden Variables
• Consider a simple case
• Alignment variable a(i)
• Rewrite
Identify Hidden Variables
• Consider a simple case
• Alignment variable a(i)
• Rewrite
Identify Hidden Variables
• Consider a simple case
• Alignment variable a(i)
• Rewrite
Identify Hidden Variables
• Consider a simple case
• Alignment variable a(i)
• Rewrite
EM Algorithm for A Translation Model
• Introduce an alignment variable for each translation pair
• EM algorithm for the translation model
• E-step: compute the posterior for each alignment variable
• M-step: estimate the translation probability Pr(e|c)
EM Algorithm for A Translation Model
• Introduce an alignment variable for each translation pair
• EM algorithm for the translation model
• E-step: compute the posterior for each alignment variable
• M-step: estimate the translation probability Pr(e|c)

We are luck here. In general, this step can be extremely difficult and usually requires approximate approaches

Compute Pr(e|c)
• First compute
Compute Pr(e|c)
• First compute
Iterative Scaling
• Maximum entropy model
• Iterative scaling
• All features
• Sum of features are constant
Iterative Scaling
• Compute the empirical mean for each feature of every class, i.e., for every j and every class y
• Start w1,w2 …, wc = 0
• Repeat
• Compute p(y|x) for each training data point (xi, yi) using w from the previous iteration
• Compute the mean of each feature of every class using the estimated probabilities, i.e., for every j and every y
• Compute for every j and every y
• Update w as
Iterative Scaling

Can we use the concave property of logarithm function?

No, we can’t because we need a lower bound

Iterative Scaling

Wait a minute, this can not be right! What happens?

Logarithm Bound Algorithm
• Come up with a lower bounded
• Search the optimal solution that maximizes
Iterative Scaling

Where does it go wrong?

Iterative Scaling

Is this solution unique?

Iterative Scaling

Univariate functions!

Faster Iterative Scaling
• The lower bound may not be tight given all the coupling between weights is removed
• A tighter bound can be derived by not fully decoupling the correlation between weights