Introduction to Pattern Recognition

A Pattern is a repeated decorative design.  We, humans are very good recognizing Patterns in our every day life.  We  can't live without patterns.  Our brain is actually classifying things based patterns and everyday we are learning  patterns only.   There is a huge difference between what we study and what we practice.  Even when there were no  colleges, humans should distinguish between friend and foe using patterns. 

For example, if I show you the following diagram, what you will say

Most Probably (due to my poor drawing), you will say it is a car.  But is it resembling any car parked outside.  What we recognize a car is nothing but the shape, wheels, window, steering wheel and head light.  Rest of the details are unimportant to us.

Let us take an example of a 6 months old toddler.  He cannot talk, he cannot move and he cannot understand most things.  But still he can immediately smiles at his mother.  The baby can do a pattern recognition in no time.  Because it is an essential part of our living.  Recognizing mother is the first important thing for our survival.

There are lot of ways in which patterns can be found.  They are classified into two broad categories, Supervised and Unsupervised.  Recognizing mother is unsupervised.  The baby forms clusters of people who are moving with him frequently and all other people are placed in a different cluster.  But recognizing car is a supervised learning.  Someone needs to tell the child that the picture is a car, what Papa is driving is a car etc.  Upon repeatedly interacting and experiencing the child learns that it is a car. 

There are many algorithms available to find the pattern.  Ok, after finding the pattern, what one is going to do.  The pattern can be used to do predictive analysis.  For eg, I collect all the data of school kids in US and analyze them.  I will also check where each kid ended up.  By figuring out the correlation (we will come back to this later) between the data and where the kid ended up, I can draw a conclusion that what is the pattern of the school drop outs. 

When I get current data, say the end of first semester data, by applying the above pattern, I can figure out which kids are likely to be drop out of school.  I can take effective actions to address the problems of that particular kid and save him/her. 

The above is an example of Supervised learning.  I have the past data with grades and where the kids ended.  I am finding a pattern, in other words, a mathematical formula to approximate my findings within a reasonable error limit.  When a new data comes in, I can check what the output is going to be. 

This technique is used in many many areas, such as credit card ratings, fraud detection, Spam filtering etc.

If I don't know the output, I can organize objects into groups whose members are similar in some way.  For eg, I am observing some symptoms (headache, temperature  etc.)  I want to check whether the person has flu or brain tumor.  Even though all the symptoms of any disease will be present in all the patients, still I can classify the people into one of the groups such as having flu, having brain tumor etc. 

There are many interesting algorithms to work with Supervised and Unsupervised.  I will continue the articles explaining them more.