3.6 Probabilistic Topic Models: Expectation-Maximization Algorithm: Part 3

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Из курса от партнера University of Illinois at Urbana-Champaign

Text Mining and Analytics

292 оценки

University of Illinois at Urbana-Champaign

Text Mining and Analytics

292 оценки

Курс 3 из 6 — Specialization Data Mining

This course will cover the major techniques for mining and analyzing text data to discover interesting patterns, extract useful knowledge, and support decision making, with an emphasis on statistical approaches that can be generally applied to arbitrary text data in any natural language with no or minimum human effort. Detailed analysis of text data requires understanding of natural language text, which is known to be a difficult task for computers. However, a number of statistical approaches have been shown to work well for the "shallow" but robust analysis of text data for pattern finding and knowledge discovery. You will learn the basic concepts, principles, and major algorithms in text mining and their potential applications.

Из урока

Week 3

During this module, you will learn topic analysis in depth, including mixture models and how they work, Expectation-Maximization (EM) algorithm and how it can be used to estimate parameters of a mixture model, the basic topic model, Probabilistic Latent Semantic Analysis (PLSA), and how Latent Dirichlet Allocation (LDA) extends PLSA.

3.1 Probabilistic Topic Models: Mixture of Unigram Language Models12:39

3.2 Probabilistic Topic Models: Mixture Model Estimation: Part 110:16

3.3 Probabilistic Topic Models: Mixture Model Estimation: Part 28:15

3.4 Probabilistic Topic Models: Expectation-Maximization Algorithm: Part 111:05

3.5 Probabilistic Topic Models: Expectation-Maximization Algorithm: Part 210:39

3.6 Probabilistic Topic Models: Expectation-Maximization Algorithm: Part 36:25

3.7 Probabilistic Latent Semantic Analysis (PLSA): Part 110:38

3.8 Probabilistic Latent Semantic Analysis (PLSA): Part 210:15

3.9 Latent Dirichlet Allocation (LDA): Part 110:20

3.10 Latent Dirichlet Allocation (LDA): Part 212:03

Познакомьтесь с преподавателями

ChengXiang Zhai
Professor
Department of Computer Science

So, I just showed you that empirically the likelihood will converge,

but theoretically it can also be proved that EM algorithm will

converge to a local maximum.

So here's just an illustration of what happened and a detailed explanation.

This required more knowledge about that,

some of that inequalities, that we haven't really covered yet.

So here what you see is on the X dimension, we have a c0 value.

This is a parameter that we have.

On the y axis we see the likelihood function.

So this curve is the original likelihood function,

and this is the one that we hope to maximize.

And we hope to find a c0 value at this point to maximize this.

But in the case of Mitsumoto we can not easily find an analytic solution

to the problem.

So, we have to resolve the numerical errors, and

the EM algorithm is such an algorithm.

It's a Hill-Climb algorithm.

That would mean you start with some random guess.

Let's say you start from here, that's your starting point.

And then you try to improve this by moving this to

another point where you can have a higher likelihood.

So that's the ideal hill climbing.

And in the EM algorithm, the way we achieve this is to do two things.

First, we'll fix a lower bound of likelihood function.

So this is the lower bound.

See here.

And once we fit the lower bound, we can then maximize the lower bound.

And of course, the reason why this works,

is because the lower bound is much easier to optimize.

So we know our current guess is here.

And by maximizing the lower bound, we'll move this point to the top.

To here.

Right?

And we can then map to the original likelihood function, we find this point.

Because it's a lower bound, we are guaranteed to improve this guess, right?

Because we improve our lower bound and then the original likelihood

curve which is above this lower bound will definitely be improved as well.

So we already know it's improving the lower bound.

So we definitely improve this original likelihood function,

which is above this lower bound.

So, in our example,

the current guess is parameter value given by the current generation.

And then the next guess is the re-estimated parameter values.

From this illustration you can see the next guess

is always better than the current guess.

Unless it has reached the maximum, where it will be stuck there.

So the two would be equal.

So, the E-step is basically

to compute this lower bound.

We don't directly just compute this likelihood function but

we compute the length of the variable values and

these are basically a part of this lower bound.

This helps determine the lower bound.

The M-step on the other hand is to maximize the lower bound.

It allows us to move parameters to a new point.

And that's why EM algorithm is guaranteed to converge to a local maximum.

Now, as you can imagine, when we have many local maxima,

we also have to repeat the EM algorithm multiple times.

In order to figure out which one is the actual global maximum.

And this actually in general is a difficult problem in numeral optimization.

So here for example had we started from here,

then we gradually just climb up to this top.

So, that's not optimal, and we'd like to climb up all the way to here,

so the only way to climb up to this gear is to start from somewhere here or here.

So, in the EM algorithm, we generally would have to start from different points

or have some other way to determine a good initial starting point.

To summarize in this lecture we introduced the EM algorithm.

This is a general algorithm for computing maximum maximum likelihood estimate of all

kinds of models, so not just for our simple model.

And it's a hill-climbing algorithm, so it can only converge to a local maximum and

it will depend on initial points.

The general idea is that we will have two steps to improve the estimate of.

In the E-step we roughly [INAUDIBLE] how many there are by predicting values

of useful hidden variables that we would use to simplify the estimation.

In our case, this is the distribution that has been used to generate the word.

In the M-step then we would exploit such augmented data which would make

it easier to estimate the distribution, to improve the estimate of parameters.

Here improve is guaranteed in terms of the likelihood function.

Note that it's not necessary that we will have a stable convergence of

parameter value even though the likelihood function is ensured to increase.

There are some properties that have to be satisfied in order for the parameters

also to convert into some stable value.

Now here data augmentation is done probabilistically.

That means,

we're not going to just say exactly what's the value of a hidden variable.

But we're going to have a probability distribution over the possible values of

these hidden variables.

So this causes a split of counts of events probabilistically.

And in our case we'll split the word counts between the two distributions.

[MUSIC]

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