### Expectation-Maximization with Less Arbitrariness

**Introduction**

There are many introductions to the Expectation-Maximisation algorithm. Unfortunately every one I could find uses arbitrary seeming tricks that seem to be plucked out of a hat by magic. They can all be justified in retrospect, but I find it more useful to learn from reusable techniques that you can apply to further problems. Examples of tricks I've seen used are:

- Using Jensen's inequality. It's easy to find inequalities that apply in any situation. But there are often many ways to apply them. Why apply it to
*this*way of writing this expression and not that one which is equal? - Substituting in the middle of an expression. Again, you can use just about anywhere. Why choose this at this time? Similarly I found derivations that insert a into an expression.
- Majorisation-Minimisation. This is a great technique, but involves choosing a function that majorises another. There are so many ways to do this, it's hard to imagine any general purpose method that tells you how to narrow down the choice.

**Generalities about EM**

The EM algorithm seeks to construct a maximum likelihood estimator (MLE) with a twist: there are some variables in the system that we can't observe.

First assume no hidden variables.
We assume there is a vector of parameters that defines some model.
We make some observations .
We have a probability density that depends on .
The likelihood of given the observations is .
The maximum likelhood estimator for is the choice of that maximises for the we have observed.

Now suppose there are also some variables that we didn't get to observe.
We assume a density .
We now have

where we sum over all possible values of . The MLE approach says we now need to maximise

One of the things that is a challenge here is that the components of might be mixed up among the terms in the sum. If, instead, each term only referred to its own unique block of , then the maximisation would be easier as we could maximise each term independently of the others. Here's how we might move in that direction. Consider instead the log-likelihood

Now imagine that by magic we could commute the logarithm with the sum. We'd need to maximise

One reason this would be to our advantage is that often takes the form where is a simple function to optimise. In addition, may break up as a sum of terms, each with its own block of 's. Moving the logarithm inside the sum would give us something we could easily maximise term by term. What's more, the for each is often a standard probability distribution whose likelihood we already know how to maximise. But, of course, we can't just move that logarithm in.

**Maximisation by proxy**

Sometimes a function is too hard to optimise directly. But if we have a guess for an optimum, we can replace our function with a proxy function that approximates it in the neighbourhood of our guess and optimise that instead. That will give us a new guess and we can continue from there. This is the basis of gradient descent. Suppose is a differentiable function in a neighbourhood of . Then around we have

We can try optimising with respect to within a neighbourhood of . If we pick a small circular neighbourhood then the optimal value will be in the direction of steepest descent. (Note that picking a circular neighbourhood is itself a somewhat arbitrary step, but that's another story.) For gradient descent we're choosing because it matches both the value and derivatives of at . We could go further and optimise a proxy that shares second derivatives too, and that leads to methods based on Newton-Raphson iteration.

We want our logarithm of a sum to be a sum of logarithms.
But instead we'll settle for a proxy function that is a sum of logarithms.
We'll make the derivatives of the proxy match those of the original function
precisely so we're not making an arbitrary choice.

Write

The are constants we'll determine. We want to match the derivatives on either side of the at :

On the other hand we have

To achieve equality we want to make these expressions match.
We choose

Our desired proxy function is:

So the procedure is to take an estimated and obtain a new estimate
by optimising this proxy function with respect to .
This is the standard EM algorithm.

It turns out that this proxy has some other useful properties.
For example, because of the concavity of the logarithm,
the proxy is always smaller than the original likelihood.
This means that when we optimise it we never optimise ``too far''
and that progress optimising the proxy is always progress optimising the
original likelihood.
But I don't need to say anything about this as it's all part of the standard literature.

**Afterword**

As a side effect we have a general purpose optimisation algorithm that has nothing to do with statistics. If your goal is to compute

you can iterate, at each step computing

where is the previous iteration. If the take a convenient form then this may turn out to be much easier.

**Note**

This was originally written as a PDF using LaTeX. It'll be available here for a while. Some fidelity was lost when converting it to HTML.

## 2 Comments:

Although I use the example of steepest ascent (or descent) to motivate EM, there's an interesting difference pointed out to me by a work colleague.

When using steepest ascent you're using the fact that the linear proxy function matches the original function in a small region. So when you maximise the proxy you need to perform a maximisation in a small region. This is essentially why we typically take small step sizes in the steepest ascent algorithm. This means that steepest ascent can get stuck in local minima.

In the case of EM we similarly ensure that the proxy matches the true objective locally in a small region. However, the concavity of the log function means that the proxy is always less than or equal to the original function. As a result, we don't have to be conservative. Globally maximising the proxy is guaranteed to be safe. Because EM isn't restricted to small steps it can sometimes make big jumps from one local maximum to another. That doesn't mean it'll always find the global maximum of your likelihood. But it is a qualitative difference from steepest ascent.

(Pure Newton-Raphson can also make big jumps. But, unmodified, it's not always a good algorithm because there are no guarantees that the quadratic proxy is always less than the true objective function.)

Thank you for sharing this insightful observation!

There is a small typo in the formula for the linear approximation. The derivative has to be multiplied by $(x-x_0)$, not by $x$.

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