Did you ever try to think about how your brain assesses the world near you? You’re scrolling through a horde of Instagram posts and images; your eyes can stop on the conversant face (scorn plenty of other information around it). You are so used to pattern recognition thanks to your brain that you don’t even pause to think about what pushes to the technological side of it.
It was easy, isn’t it? So, I will explain the technology of pattern recognition and its relation to machine learning in simple English.
What is Pattern Recognition?
-In General
You hear this expression a lot in the IT field, but it comes from ‘cognitive neurosciences and psychology’. It’s a cognitive process that happens in your brain when you match some information that you encounter with data stored in your memory. Let me give an example for this when a mother teaches her child about alphabets, she says, “A, B, C.” After multiple reprises, when the mother says, “A, B…” the kid can answer with “C.” As you can see, the kid recognized the pattern.
A human body has pattern recognition receptors (PRR), these are called cells in general that have a particular mission to recognize and confront infective molecular patterns and harm-associated molecular patterns. But I am talking about biology and not technology; well that was to understand, now, let’s go the computer side.
-In Computer Science
In computer science and machine learning, pattern recognition (PR) is a tool that matches the information stored in the record with the arriving data. A question can arise in your mind, “What is the difference between pattern recognition and machine learning?” The answer to this question is simple, pattern recognition is a type of machine learning.
As you can see from the figure above, the outcome of the pattern recognition can be either class assignment, or cluster assignment, or predicted variables. Hence, there is no point in asking “what is the difference between pattern recognition and classification” - classification algorithm is a part of the supervised machine learning problems, where the target value is a finite set of classes.
You also have to differentiate between pattern recognition and computer vision. While these two technologies seem similar, computer vision technology mostly focuses on processing and analyzing images and visual information, such as object detection, visual-based learning, and segmentation. Pattern recognition, on the other hand, is aimed at the automated discovery of patterns in all kinds of data - visual as well as others.
There is also a term called “curse of dimensionality.”
This phenomenon can be found in areas like as sampling, numerical analysis, and data mining (among many others.) The problem here is that when the dimensionality increases, the space volume increases fast as well, and available data becomes light. Why is it called the dimensionality curse? Because, for statistically sound and valid results, you need a decent amount of information.
Pattern recognition is the technology that permits the learning process. Thus, it is an integral part of the entire technique of machine learning. It authorizes the algorithms to discover regularities within massive amounts of data and supports to classify it into various classes.
How it Works
Pattern recognition is a process that looks at the exciting data and tries to see whether there are any symmetries within it. There are two major parts:
- Explorative part, where the algorithms are looking for patterns in general
- Descriptive part, where the algorithms start to categorize the found patterns
- Texts or words
- Imageries
- Feelings (emotions)
- Voices/Sounds
- Other origins and information
The information that is collected from this pattern-searching process can be used for data analytics systems. This feature is especially crucial for big data analytics, where the users cannot process such huge amounts of data by themselves or with the help of MS Excel or other alike tools.
As you can see from the figure above, the mechanism of the PR system includes these three aspects:
- Data gathering and preprocessing
- Data representation
- Decision making
Use Case for Pattern Recognition in Stock Market Forecasting
Pattern Recognition technology and Data Analytics are interconnected to the point of confusion between the two. An excellent example of this issue is stock market pattern recognition software, which is actually an analytics tool.
In the context of data analytics, pattern recognition is used to describe data, show its distinct features (like the patterns itself), and put it into a broader context. I will finish the writing here; I tried myself to keep it simple as I can, hope it helps.
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