what is it? when would you use it? why does it work? how do you implement it?

What is it?

When would you use it?

Why does it work?

How do you implement it?

Where does it stand in relation to other methods?

EM algorithm reading group

Introduction & Motivation

Theory

Practical

Comparison with other methods

Expectation Maximization (EM)

• Iterative method for parameter estimation where you have missing data

• Has two steps: Expectation (E) and Maximization (M)

• Applicable to a wide range of problems

• Old idea (late 50’s) but formalized by Dempster, Laird and Rubin in 1977

• Subject of much investigation. See McLachlan & Krishnan book 1997.

Applications of EM (1)

• Fitting mixture models

Applications of EM (2)

• Probabilistic Latent Semantic Analysis (pLSA)– Technique from text community

P(w,d) P(w|z) P(z|d)

Z

WD

Z

D

W

Applications of EM (3)

• Learning parts and structure models

Applications of EM (4)

• Automatic segmentation of layers in video

http://www.psi.toronto.edu/images/figures/cutouts_vid.gif

Motivating example

0 1-2 -1-4 -3 4 52 3

Data:

Model:

Parameters:

OBJECTIVE: Fit mixture of Gaussian model with C=2 components

keep fixed

i.e. only estimate x

P(x|)

where

Likelihood functionLikelihood is a function of parameters, Probability is a function of r.v. x DIFFERENT TO LAST PLOT

Probabilistic modelImagine model generating data

Need to introduce label, z, for each data point

Label is called a latent variablealso called hidden, unobserved, missing

c

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Simplifies the problem:if we knew the labels, we can decouple the components as estimate

parameters separately for each one

Intuition of EME-step: Compute a distribution on the labels of the points, using current parameters

M-step: Update parameters using current guess of label distribution.

E

E

M

M

E

Theory

Complete log-likelihood (CLL)

Log-likelihood [Incomplete log-likelihood (ILL)]

Expected complete log-likelihood (ECLL)

Some definitionsObserved data

Latent variables

Iteration index

Continuous I.I.D

Discrete 1 ... C

Use Jensen’sinequality

Lower bound on log-likelihood

AUXILIARY FUNCTION

Jensen’s InequalityJensen’s inequality:

For a real continuous concave function and

Equality holds when all x are the same

1. Definition of concavity. Consider where

then

2. By induction:

for

Recall key result : Auxiliary function is LOWER BOUND on likelihood

EM is alternating ascent

Alternately improve q then :

Is guaranteed to improve likelihood itself….

E-step: Choosing the optimal q(z|x,)

Turns out that q(z|x,) = p(z|x,t) is the best.

E-step: What do we actually compute?

nComponents x nPoints matrix (columns sum to 1):

Component 1

Component 2

Poi

nt 1

Poi

nt 2

Poi

nt 6

Responsibility of component for point :

M-Step

Entropy termECLL

Auxiliary function separates into ECLL and entropy term:

M-Step

From previous slide:

Recall definition of ECLL:

From E-step

Let’s see what happens for

Practical

InitializationMean of data + random offsetK-Means

TerminationMax # iterationslog-likelihood changeparameter change

ConvergenceLocal maximaAnnealed methods (DAEM)Birth/death process (SMEM)

Numerical issuesInject noise in covariance matrix to prevent blowupSingle point gives infinite likelihood

Number of componentsOpen problemMinimum description lengthBayesian approach

Practical issues

Local minima

Robustness of EM

What EM won’t do

Pick structure of model# componentsgraph structure

Find global maximum

Always have nice closed-form updatesoptimize within E/M step

Avoid computational problemssampling methods for computing expectations

Comparison with other methods

Why not use standard optimization methods?

• No step size

• Works directly in parameter space model, thus parameter constraints are obeyed

• Fits naturally into graphically model frame work

• Supposedly faster

In favour of EM:

Gradient

Newton

EM

Gradient

Newton

EM

Acknowledgements

Shameless stealing of figures and equations and explanations from:

Frank DellaertMichael JordanYair Weiss