Exploring the Ways to Use Unlabeled Data in Machine Learning

Machine learning relies on data and proper analysis to make accurate predictions that can help make decisions. Labeling data is considered very important for developing high-performing machine learning models. This process helps machines understand the different aspects of the world around them.

Since the technology is dependent on datasets, it is crucial to consider both labeled and unlabeled data. Labeled data are categorized, making them easy for machines to understand. Human annotators do the labeling and are crucial for supervised learning tasks. 

On the other hand, unlabeled data are those that are not labeled or categorized. These kinds of data are useful for unsupervised learning tasks, and the machines must learn from the inherent data structure. 

In this blog, we will focus on how to use unlabeled data in machine learning. By the end of the blog, you will understand what unlabeled data is and how it is used. Also, you will learn about the different unlabeled datasets and how they are different from labeled data. 

Defining and Differentiating Labeled and Unlabeled Data in Machine Learning

Machine learning models cannot analyze and understand the images or data you input if they are not labeled or categorized. Labeled data, as mentioned earlier, refers to those that are labeled or categorized by human annotators. Machine learning models use labeled data to predict new, unseen data. A few examples of such data include: 

→ Image datasets showing the contents of the images
→ Email datasets labeled as spam or not spam
→ Labeling customer review data based on sentiment (positive, negative, neutral)

These are primarily used in supervised machine learning training processes. 

Unlabeled data is the complete opposite of labeled data. Here, the data is not labeled or categorized. It is generally used in unsupervised machine learning models. A few examples of unlabeled data in machine learning are: 

→  Customer transaction data without any label of being fraudulent or non-fraudulent
→  Dataset of texts without any label showing the category or topic of each document
→  Collection of images without any label to the contents of the images

Unlabeled data is also used in tasks such as clustering, dimensionality reduction, anomaly detection, and unsupervised learning. Classifying images with artificial intelligence is a great example of unlabeled data in machine learning. 

For more clarity, refer to the table below to understand what’s the difference between labeled and unlabeled data:

Unlabeled Data vs Labeled Data

That defines what is labeled and unlabeled data in machine learning. In the following section, let’s go through the different types of machine learning models.

What Are the Different Types of Machine Learning Models?

Artificial Intelligence has been solving a lot of problems lately. However, the problems they solve are not similar to each other. It is crucial to understand the different machine learning models in order to group and get accurate results. Here’s a look at the various machine learning models: 

A. Unsupervised Machine Learning

Unsupervised learning refers to a machine learning algorithm that works with unlabeled datasets. It is generally used to detect hidden patterns using cluster analysis on massive datasets. 

B. Supervised Machine Learning

Supervised learning uses labeled datasets. It is where both input data and output variables are available. The data, in this case, is annotated by humans and has a prediction goal. You can check websites offering data annotation services to understand how data is labeled and used for supervised learning. 

C. Semi-Supervised Machine Learning

This is a combination of both types mentioned above. Its differentiating factor is that it uses pieces of unlabeled data that are annotated at certain points. This model has proved very useful in self-training and co-training. 

D. Reinforcement Machine Learning

Reinforcement learning does not have any data. Reinforcement learning uses the environment and an agent with a goal. The model uses punishments and rewards to guide the agent toward the desired outcome. 

Let’s move on and learn about the reasons to use unlabeled data. 

Why Is It Necessary to Use Unlabeled Data?

Since we have talked about how labeled data makes things easy, you would not be wrong to ask, ‘Then, why use unlabeled data?’ We will help you understand that. To begin with, unlabeled data is used in unsupervised learning. While this learning model does not have a specific target, it can be used to develop artificial intelligence. 

Unsupervised learning algorithms have a major role in using unlabeled data classification to group cases based on similar characteristics and naturally occurring data patterns. Further, if we compare analyzed and unlabeled data, the latter will benefit machine learning. The reason is that unlabeled data is comparatively easy to get and is cheaper. 

It is not necessary to have fancy storage to protect the data. Instead, it is important to understand the ways to use unlabeled data. In the following section, we will learn how to use this data with the help of the two unsupervised learning methods: clustering and dimensionality reduction. 

Exploring the Two Important Methods of Unsupervised Learning

It is important to understand the two important methods of unsupervised learning to know how unlabeled data is used in machine learning: 

A. Clustering

This unsupervised learning method is used to group similar elements based on the proximity in measurement space. The method is very popular in unsupervised learning models because, in this case, there’s no need to train machine learning models. Implementing stand-alone techniques like k-means algorithm and parameter optimization into an ML system can get the job done. 

B. Dimensionality Reduction

The unsupervised learning method is used to simplify unlabeled datasets by describing the different elements with fewer, more general features. It helps reduce the number of features without losing valuable information from the data sets. 

These will help you understand how unlabeled data can be used in machine learning models. You can also learn about the labeling process by visiting websites offering annotation or data labeling services. 

Before we end this discussion, let us share a few advantages, limitations, and real-world examples of using unlabeled data. 

What Are the Advantages and Disadvantages of Using Unlabeled Data?

Machine learning uses both labeled data and unlabeled data. However, using a set of unlabeled data can be beneficial. Here’s why:

Advantages of Unlabeled Data

A. Cost and Scalability

It is not necessary to use human resources to analyze unlabeled raw data. It helps collect massive amounts of data without worrying about the cost of analyzing them. 

B. Natural Data Distribution

Organizations can use unlabeled data to train machine learning models because of its natural data distribution. Labeled data is refined and annotated, not letting machines capture real-world scenarios. Exposure to unlabeled data helps develop robust features. 

C. Reduced Human Bias

Since humans are not involved in the process, models can capture the patterns that might get overlooked. This is useful in areas where human expertise is limited. 

D. Continuous Learning

Data analysis of unlabeled data helps machine learning models to adapt to new patterns and emerging trends. Companies can collect data continuously and process new data to stay updated without human intervention. 

That establishes the need to use unlabeled data. However, there are a few drawbacks as well. Let’s take a look at them: 

Disadvantages of Using Unlabeled Data

A. Quality Control

If the data is not labeled by humans, it can pose a threat to quality control. Organizations have to use strong filtering mechanisms to filter irrelevant or corrupt data. Such data can lead machines to learn incorrect patterns. 

B. Computational Demands

Processing a massive amount of data needs major computational resources. Despite the fact that unlabeled data is cost-effective, processing them will require a lot of money. 

C. Validation Complexity

It can be difficult to assess model performance while working with unlabeled datasets. There are techniques to handle such data, but measuring success and identifying errors is not as easy as labeled data. 

D. Domain Expertise

It is necessary to have the necessary expertise in this domain to analyze unlabeled data. Organizations must find the right people to refine unlabeled data. 

This will help you understand both sides of the coin and implement the data properly. In the following section, we will share a few real-world examples for a better understanding.

A Few Real-World Examples of Unlabeled Data

Here are a few real-world examples of unlabeled data implementation: 

→ E-commerce and customer behavior
→ Content streaming and recommendations
→ Cybersecurity and threat detection
→ Healthcare applications
→ Natural language processing
→ Scientific research

All these fields use unlabeled data. 

Final Thoughts,

Labeled and unlabeled data are both equally important. By now, you have understood what is labeled data in machine learning and how they are used in supervised learning. Also, you learned about the ways to cluster unlabeled data, labeled vs unlabeled data, and the ways to implement them. Undeniably, there’s more to learn. But this would give you a comprehensive idea about the use of unlabeled data. Use this knowledge to understand what the future holds. 

All the best!