“Unsupervised learning” is a powerful weapon to unknown worlds, isn’t it?


I have almost finished my MOOCs of machine learning in Coursera.   The algorithm to be learned now is “Unsupervised learning”.  It is the first time for me to learn unsupervised learning algorithms. It must be excited! What is the difference between  “Supervised learning” and “Unsupervised learning”?   Unlikely supervised learning algorithm,  we do not need to have the results of occurring events in the past. For example, when we try to apply the logistic regression model in the predictions of  defaults of customers,  we need to have results of defaults to train the models so that  the model can classify “Who will be in a default or not” effectively.  But  “Unsupervised learning”  does not need to have such results in advance. When I heard that, I was very surprised because I only knew supervised learning.  When I used to be a credit risk manager in a consumer finance company,  I should consider how we could obtain the data about customers which included data about “who was in default in the past?”.  However, unsupervised learning does not need to have the results of classifications in advance. Without the result of that, unsupervised learning algorithm can capture the structures of data. It enables us to jump into unknown worlds because unsupervised learning algorithm works in such an area because it does not need to have the results to train the models.

Then what kind of problems we can apply this powerful algorithm? Let us stretch our  imaginations here!

1.  Social structure

This is related to microeconomics. As there is a lot of social classes or group in our society.  There are many ways to make clusters in it.  For examples,  based on age, sex, annual salary, educations, address, industries,  cities, countries, etc.  Each class may behave differently to the economic events. Therefore, we may predict the sequences after the events, such as a tax increase when we can make clustering of our society. If unsupervised learning can make new clusters in our society on a real-time basis, it must be good for economic analysis as this is based on the latest information.

2. Banking and financial system

The banking system is critically important for the economy of each country.  It sometimes fails and malfunctions, however,  the economy has serious damage for long periods. Everyone knows what happened after Lehman crisis, which was one of the biggest financial crisis in history. There are a lot of players in the banking and financial systems, such as commercial banks, investment banks, credit card companies, asset management companies, consumer finance companies, etc. This  system is sometimes unstable due to massive lending activities. Usually it is difficult to understand what is going on there in real-time basis. If an unsupervised learning algorithm can capture change of structure in banking and financial system in advance, it may enable us to take action before the problem arises, rather than after.

Unsupervised learning may provide us new insight of our society as there is no need to obtain the result of events in advance.  It is good because the future is generally different from the past.  Unsupervised learning must be a powerful weapon to analysis new, unknown worlds  as our society has been changing everyday basis and sometimes no one knows what is going on there.


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