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As opposed to supervised learning,
in unsupervised learning, the data set is
unlabelled and hence there is no right or wrong answer.
The objective of unsupervised learning is thus
to extract knowledge from the data for instance,
by discovering hidden patterns.
In this video, we'll discuss two kinds of unsupervised learning,
clustering, and data transformation.
Clustering concerns partitioning data into groups called clusters.
The goal is to split up the data in
such a way that points within a single cluster are very similar,
and points in different clusters are different.
For instance, by clustering an image dataset, we may end up with four clusters.
Images of people, of animals,
of plants, and of buildings.
But we could also end up with three completely different clusters,
photos that were taken on a sunny day,
on a cloudy day, or on a snowy day.
K-means clustering is one of the simplest and most widely used clustering algorithms.
The main idea is to find
cluster centers that are representative of certain regions of the data.
This is done by alternating between two steps,
assigning each data point to the closest cluster centre,
and then setting each cluster centre
as the mean of the data points that are assigned to it.
Let's illustrate how the algorithm works on a fictitious example.
Suppose our data looks like this,
and we're looking for three clusters.
We initialize the algorithm by randomly setting
three data points to be cluster centres, like this.
Next, the assignment phase takes place, where
each data point is assigned to the closest cluster center.
After the assignment phase,
we recompute the cluster centres.
We go through another couple of rounds of point assignment and center
recalculation, until there are no further changes in point assignment.
These are the three clusters that we have discovered.
It is worth mentioning that the number of clusters k is an input parameter,
and if we don't set it to
an appropriate value we may get poor results, as demonstrated here.
Clustering is widely used in medicine and healthcare.
For instance, to identify
similar patients based on their attributes and explore potential treatments.
Apart from clustering, we may carry out
unsupervised learning so as to transform a data set.
For instance, to compress the data and find
a representation that is more informative for further data processing.
This is commonly referred to as dimensionality reduction and feature extraction.
A simple method that is commonly used for dimensionality reduction
is Principal Component Analysis or PCA, as it's typically called.
The goal here is to reduce the number of
features, while retaining as much useful information as possible.
Let's explain how PCA works for a data set with two features.
In other words, for two dimensional data.
Suppose that our dataset looks like this.
The first thing that the algorithm does is find the direction of maximum variance.
This is the direction of the data that
contains most of the information, or in other words,
the direction along which the features are most correlated with each other.
We call this component one.
Next, the algorithm finds a direction orthogonal to
component one that has a maximum variance. This is component two.
Components one and two are the principal components of our data set,
which are the main directions of variance in the data set.
We can see that if we return these dimensions,
were not going to lose any information.
It's simply a rotation of our original dimensions, our original axes.
So essentially, we've transformed or reconstructed our features.
After identifying the principal components, we can use PCA to carry out
dimensionality reduction by dropping
one, or more components that have the smallest maximum variance.
In our two dimensional dataset this means retaining only component one.
This is the reduction that gives the minimum error in
the case that we decide to reconstruct the original dataset.
In other words, PCA allowed us to reduce
the number of dimensions in our original dataset from
two, to one and get
a transformed dataset that retains as much useful information as possible.
PCA is commonly used to speed up supervised learning.
So, if we have a training set with a large number of features we can use
PCA to reduce the number of features in each example of the training set.
PCA has been widely used in the medical field.
For instance, to compress medical imaging data.
As we have discussed in this video,
unsupervised learning allows us to extract knowledge from unlabeled data.
A big challenge here, is evaluating whether the algorithm learned something useful, or not.
So, performance here is often subjective and domain specific,
therefore, unsupervised learning is often carried out in
an explorative setting or as a preprocessing step for supervised learning.
Dr Areti ManatakiTeaching and Research Fellow
Dr Frances WongData Science MOOC Project Lead
