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Now that we've gone through even more material on clustering, let's spend
a little bit of time describing what we didn't cover in this course.
So in this course,
we tried to focus on the methods that we believe are the most practical and
most widely used tools out there for performing retrieval and clustering.
But there were still some other important topics that we didn't cover in
this course.
So for example, in retrieval,
there are lots of other distance metrics that we could have described.
We listed some of these in a module, but
we didn't actually describe these in any detail.
Another thing we didn't cover was something called distance metrics learning
where these are procedures that allow you to actually learn
metrics that are useful for the task at hand.
Then for clustering, we didn't talk about things like nonparametric clustering.
Actually our hierarchical clustering methods can be used for
nonparametric clustering, but we didn't describe this very explicitly.
So nonparametric clustering, these are methods where the complexity of the models
or the description of the clustering can grow as you get more and more data points.
Another method is called spectral clustering that can be robust to different
cluster shapes like the Swiss roll type images we showed in the second module,
when we're motivating clustering and
talking about some of the limitations of the methods we were going to describe,
where we have to actually specify the shape of the clusters.
But unfortunately spectral clustering methods don't
tend to have very good scalability properties to large data sets.
And then there are a set of ideas that are related to things that we talked about but
not specifically for retrieval or clustering.
So, as an example, mixtures of Gaussians are commonly used for
something called density estimation.
So we showed this picture of this histogram over the blue intensity of
all the images in our data set and
we talked about the distribution on those intensities.
So we can think of that as a density over intensities, and try and
estimate the form of that density explicitly, rather than
thinking about mixtures of Gaussians as a means of clustering data points.
So they're hand in hand, the same tool, but different tasks.
And then within the context of density estimation,
you can start talking about things like anomaly detection,
where this is a question of you get some new data point, and
does it look significantly different than the data points that you've seen so far.
But with what you've learned in this class you have the tools to go out and
learn these other methods.
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