Pattern Recognition in Machine Learning2019-11-13T15:12:02.000Z 2019-11-13T15:12:02.000Z In this article, well talk about the technology of pattern recognition in plain English and how this relates to the machine learning field.
Have you ever stopped to think about how your brain assesses the world around you? For example, when you’re looking through a multitude of Facebook posts and photos, your eyes can stop on a familiar face (despite tons of other information next to it). We are so used to pattern recognition thanks to our brains that we don’t even stop to think about what powers the technological side of it.
In this article, we’ll talk about the technology of pattern recognition in plain English and how this relates to the machine learning field in general.
What does pattern recognition mean?
What is pattern recognition in general? While we hear this term a lot in the IT world, it originally comes from cognitive neuroscience and psychology.
Pattern recognition is a cognitive process that happens in our brain when we match some information that we encounter with data stored in our memory. For example, when a mom teaches her kid to count, she says, “One, two, three.” After multiple repetitions, when mom says, “One, two...”, the child can respond with “Three.” As we can see, the child recognized the pattern.
There are also pattern recognition receptors (PRR) in our body - macrophages, monocytes, etc. - cells that have a specific mission to identify and tackle pathogenic molecular patterns and damage-associated molecular patterns. But that’s biology and not technology.
What is pattern recognition in computer science? In computer science and machine learning, pattern recognition is a technology that matches the information stored in the database with the incoming data.
Sometimes people ask, “What is the difference between pattern recognition and machine learning?” The answer is simple: pattern recognition is a type of machine learning.
The Basic Components of Pattern Recognition System
As you can see from the chart above, the result of the pattern recognition can be either class assignment, or cluster assignment, or predicted variables. Therefore, 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.
We also have to distinguish 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.” What is the curse of dimensionality in pattern recognition?
This phenomenon can be found in areas such as sampling, combinatorics, data mining, and numerical analysis (among many others.) The issue here is that when the dimensionality increases, the space volume increases fast as well, and available data becomes sparse. 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 enables the learning process. Therefore it is an integral part of the entire technique of machine learning. It empowers the algorithms to discover regularities within vast amounts of data and helps to classify it into various categories.
How does pattern recognition work?
Pattern recognition is a process that looks at the available data and tries to see whether there are any regularities within it. There are two main parts:
- Explorative part, where the algorithms are looking for patterns in general
- Descriptive part, where the algorithms start to categorize the found patterns
Unlike with computer vision (which we discussed above), the datum for pattern recognition can be anything:
- Texts or words
- Sentiments (emotions)
- Other elements and information
The information that is gleaned from this pattern-searching process can be used for data analytics systems. This feature is especially vital for big data analytics, where the users cannot process such vast amounts of data by themselves or with the help of Excel or other similar tools.
Here’s a simplified process of how pattern recognition works:
As you can see from the chart above, the design of the PR system includes these three aspects:
- Data acquisition and preprocessing
- Data representation
- Decision making
In terms of the approaches to pattern recognition, there are four main ones:
Each one of these approaches has its pros and cons as well as specific use cases when they are applicable.
Check out the comparative chart below that focuses on Statistical Pattern Recognition (StatPR) vs. Syntactic Pattern Recognition (SyntPR) vs. Neural Pattern Recognition (NeurPR):
In Statistical Pattern Recognition (StatPR), each pattern is described with the help of d features or measurements and is viewed as a point in a d-dimensional space. The goal of StatPR is to choose the features that allow pattern vectors to belong to different categories in this d-dimensional feature space.
Syntactic Pattern Recognition (SyntPR) relies on the elementary/simplest subpatterns that are called primitives (for example, letters of the alphabet). The pattern then is described in terms of the primitives' interrelation, when they are assembled into words and sentences. There are grammatical rules that command the model, which have to be inferred from the available training samples.
Neural Pattern Recognition (NeurPR) are sizeable parallel computing systems that consist of a vast number of simple processors and many interconnections between them. The main characteristics of NN are that they can learn complex nonlinear input-output relations, use sequential training procedures, and adapt themselves to the data.
Machine Learning & Pattern Recognition Challenges
While the process might seem deceptively straightforward and simple, there are still things you have to keep in mind when you’re considering implementing pattern recognition as a part of your business technology stack.
- Data processing power: if you have patients and no tight time constraints, you don’t have to worry about this too much. However, if you’ve got a big data (or near-big data) project to analyze, you better make sure your infrastructure is ready.
- Data storage: to process a lot of information, you need to ensure that you have ample space for the data storage.
- Data quality: your training sets, as well as incoming data for the algorithms, have to come from reliable sources and not contain too much noise. (Noise, in this case, might be irrelevant information for your decision-making process. For example, do you really need to know what is your client’s favorite childhood toy in order to understand whether he runs a risk of getting bad credit balance?)
- Neural network opacity: pattern recognition is a great instrument for getting business insights. However, you need to account for the neural network opacity, which means you sometimes won’t be able to explain the outputs and what to do with them. Each of the results is a combination of a multitude of neurons within a vastly complex system. Don’t be discouraged, however. The more you train your algorithms, the better the results would be.
Where can pattern recognition be implemented?
Pattern recognition, as we have discovered, can be useful throughout industries and businesses. Let’s focus now on real-life business cases and see how does pattern recognition help solve problems.
Computer-Aided Diagnosis (CAD) Systems for Healthcare
Medical science is one of the most important areas where pattern recognition technology can literally save lives. It is the foundation for the computer-aided diagnosis systems, which help doctors understand how to proceed with treatments.
In research published at the beginning of 2019, a group of medical researchers has applied machine learning and pattern recognition to detect various types of cancer in patients at an early stage.
With the help of such biomedical/medical imaging, a group of Japanese scientists was able to identify five types of cancer cell lines - SW480, DLD-1, HCT116, Panc-1, and HepG2 (in the image above). White blood cells’ size distributions are shown in green, cell lines - in red bars. This work, among many similar projects in the healthcare system, is a step toward automated diagnosis and disease prevention.
A computer vision technology, trained by machine learning and pattern recognition, automatically recognizes and classifies white blood cells and tissue as healthy or sick. In order to double-check the results, they created an additional SVM classifier that was trained by using a set of statistics of subcellular structures. According to the research authors, “This label-free, non-cytotoxic cell classification based on the subcellular structure of QPM images has the potential to serve as an automated diagnosis of single cells.“
Natural Language Processing (NLP) for Chatbots and Working with Texts
NLP is a field of machine learning that focuses on getting the computers to understand the meaning of human language as well as compose messages.
This technology is especially vital for businesses that work with words. An excellent example of NLP at work is Grammarly - a powerful tool for correcting typos and grammar as well as enhancing the general feel of the message. We use Grammarly here at HUSPI, and we are in love with this product.
Google Translate is another example of Natural Language Processing at work. Besides text analysis and word substitution, the algorithms also rely on sentiment analysis as well as context, in order to create the translation that would match the original the best.
Natural Language Processing technology can also be used for content categorization, topic discovery for content marketing, plagiarism detection, as well as text generation.
Optical Character Recognition
Optical Character Recognition technology deals with recognizing visual text - whether handwritten or printed - and its conversion into editable text. It is a combination of technologies such as machine learning, pattern recognition, and artificial intelligence.
With the help of OCR, you can store the information more compactly, easily search for the necessary entry without having to dig through tons of papers, etc. This data can even be a training dataset for other kinds of machine learning algorithms. The most common example of OCR would be the digitization of scanned documents or signature verification.
Optical Character Recognition technology is used as a way to enter information from non-digital resources - such as bank statements, passport information, invoices, business cards, or any other documentation.
One of the examples, where this technology is quite helpful is image extraction from handwritten medical forms. If you have ever tried to decipher what your doctor wrote, you’ll understand the importance of this.
At HUSPI, we have created a software that can recognize the information on a receipt from a store or restaurant (or anywhere else where you can get receipts), categorize it, and place it in its matching place in the database. This is a convenient tool for bookkeeping and accounting that automates the process and gets rid of the mindless copy-pasting of numbers.
Google Translate app is an excellent case of the OCR at work. With the help of your phone camera, you can get a translation of the text in front of you. For example, you’re in a foreign city, and you don’t understand what the sign says. Train your camera onto the sign, but instead of taking a picture, get a direct translation.
Sound / Voice / Speech Recognition
Voice and Speech Recognition technology has become especially widespread thanks to AI assistants such as Apple’s Siri, Amazon’s Alexa, Google’s OK Google, and Microsoft’s Cortana.
This technology is similar to optical character recognition, but instead of the printed or handwritten text, your source of data is spoken word. It can be used for speech-to-text translation as well as automatic subtitling (YouTube and Facebook both offer this service for the videos uploaded to their platforms.)
According to the research done in the Voice Search field, the forecast for 2020 is that 30-50% of users will rely on it for their daily activities. Adobe has conducted a survey in the United States, and here are the reasons people said they use voice search for:
As you can see, voice search can be applied virtually to any business - be it e-commerce, hospitality sector, or food delivery. The good news is also that you do not have to create your own AI assistant, but it would be wise to consider integration with Siri (for example). The iOS 13 offers convenient conversational shortcuts that can help your customers do more with your business using just their voice and their phone. Why do we need pattern recognition here? To simplify the experience and make it straightforward.
“For example, when a user says, “Order takeout,” Siri can ask, “Which order would you like?” and present a list of favorite orders to choose from a food ordering app.” (Apple Website)
Another industry that benefits from Sound Recognition pattern recognition algorithms is the automotive industry. For example, you can use your phone to record the sounds your car’s engine makes and run a sound-based diagnosis to understand what’s wrong and how to fix it.
Image Pattern Recognition
Face recognition and visual search are among the two top uses for image pattern recognition (IPR). It is similar to OCR, but instead of recognizing and transcribing textual characters, it describes pictures, so they can become searchable.
A group of biologists and researchers have worked together on one of the applications for image pattern recognition - animal recognition in the Mojave Desert.
In order to keep track of the animals and run analytics on the populace, they created a machine learning algorithm that detects the animals among the brush and classifies them according to the characteristics.
In the image below, you can see a white-tailed antelope squirrel that is a few pixels wide on the picture and isn’t facing the camera. Without the highlight around it, it is hard for the human eye to detect the animal at all from a still frame among the shadows and vegetation that looks just like the animal.
The goal of the algorithm is to recognize the animal, which can be any of the creatures found in the Mojave desert - squirrels, tortoises, bobcats, coyotes, etc.
Looking for animals in the desert is not the only application for image pattern recognition technology. E-commerce marketplaces and search engines also use it for visual search options. For example, you can search for something by uploading a picture to Google or Amazon.
IPR is also used by Facebook for tagging the appropriate people in the photos as well as by law enforcement organizations to find the fugitives or persons of interest.
The mood, opinion, and intent can also be a part of the training dataset for machine learning and pattern recognition in particular. Besides simply recognizing the face of a person, the algorithm’s purpose is to define the nature of the facial expression and what it means.
The algorithms analyze the criteria and visual aids to classify the emotions into positive or negative, angry or happy, fearful, surprised, etc. This information helps business owners and marketing specialists, especially to identify the critical points to work on.
Sentiment analysis has many applications - it can be used in customer service, recommendation engines, audience research, content optimization, and others.
For example, SalesForce’s Einstein Platform is implementing this technology into their products to provide a better User Experience to its customers.
Two Hat Security also uses sentiment analysis for content moderation, specifically for detecting toxicity (i.e., hateful speech, bullying, or abuse) in online communication and content. As a result, this helps to make the Internet a safer and happier place. Read about their research here.
Big Data Analytics
Last on our list, but not least, data analytics and pattern recognition.
We left this for this topic for the end of our article on purpose because Data Analytics - especially Big Data Analytics - is one of the central and primary uses for the Pattern Recognition technology.
Usual data can be analyzed with the help of Excel or other more advanced tools. To analyze Big Data, however, you need the help of machine learning and pattern recognition. This can be used in such industries as stock market forecasting, AdTech, and MarTech businesses where the events/day can reach several million items, and others.
Google Analytics and their Insights are also an example of the pattern recognition technology in action because it doesn’t merely track what happens on your website or mobile app, but also shows spikes and possible reasons for it.
Why is pattern recognition important?
After we wrote an enormous article, the question remains: “Why do we use pattern recognition?”
The answer is simple - we have so much information all around us that we need help to pay attention only to what matters. Instead of watching your website’s visitor statistics day and night, you can use the Google Analytics Insights to check whether there were any suspicious spikes. Instead of looking for Waldo the Squirrel in the Mojave desert, you can run an algorithm that would spot that animal in a matter of minutes (if not seconds). Instead of walking around a foreign European city with a thick dictionary, you can point your camera at the sign or a restaurant menu and understand what’s written there.
Pattern recognition technology simplifies the analytics’ results and provides a reliable source for Business Intelligence Insights, which are a critical thing for organizations and decision-making process.