An introduction to semi-supervised learning

An introduction to semi-supervised learning

When shown a series of images, you can identify and differentiate what each image is called. Using this prior information, you are also able to label other images even though you don’t already know what they are.

If you have noticed, computers and other machines in today’s time are able to do the same. Have you wondered how? It is because of semi-supervised learning models.

Semi-supervised learning is an example of machine learning training methods that, as the name suggests, falls in between supervised and unsupervised learning.

Semi-supervised algorithms employ both labeled data (data that already has one or more labels based on some identifying characteristics) and unlabeled data (data that has not been assigned a label yet) to train models, make predictions and improve performance.

There is a relatively larger chunk of unlabeled data and a smaller chunk of labeled data that is usually available.

Using only supervised or unsupervised learning, one cannot make sense of this data because of this combination of labeled and unlabeled data. This is where semi-supervised algorithms come to the rescue.

But first, what is machine learning?

Under the umbrella of artificial intelligence, machine learning (ML) imitates the way human beings think and learn using algorithms.

ML essentially means that machines can process data, draw from existing similarities and make decisions with minimal human intervention.

This is huge in a data-dependent and data-driven world such as ours. This means machines are already doing predictive analysis and drawing up results based on existing data.

Where is this used?

Virtually, everywhere around you.

Machine learning is what’s operating behind the scenes when you see the predictive text while chatting with someone or when a biometric system automatically detects your face.

All the chatbots communicating as customer support, deductions and responses of voice assistants like Siri or Alexa, and the translation option we use to find out what “food” is called in 10 different languages, make use of machine learning algorithms.

Machine learning has revolutionized how we consume and interact with data and is aiding us in variety of ways because of enhanced automation.

What are the types of machine learning?

There are three types of machine learning:

  1. Supervised learning
  2. Unsupervised learning
  3. Reinforcement learning

Supervised learning occurs when the data that is fed to the system is labeled manually by humans whereas unsupervised learning occurs when data is unlabeled. Reinforcement learning includes an algorithm that improves itself using trial-and-error methods.

Semi-supervised learning algorithms use a combination of supervised and unsupervised learning techniques to train models because the data in the training set are both labeled and unlabeled. These algorithms are trained with both kinds of data to assign labels to the total (mostly unlabeled) data.

How does semi-supervised learning work?

A good way to understand this is to think of images and their labels. Think of it this way - it is as if you don’t know what something is because it hasn’t yet been assigned a label but, with the help of previous knowledge and some assumptions, you find similarities and assign a label to it.

Using this training, you can make sense of more similar items that you can see.

This is the same for machines. Suppose there are a bunch of images of animals put together without any labels. With no prior knowledge about any of these images, the machine cannot understand what each image is called and which of them falls into a certain group.

This is where we need human intervention.

Programmers use clustering techniques to assign labels to these images manually to train the machine to identify these images as one group so that future unlabeled data can be labeled in the same manner.

Because not all data comes pre-labeled and the larger chunk of data is unlabeled, the first step is to label this unlabeled data in a process called pseudo-labeling. How does this work?

The machine uses a small amount of labeled data to train models - like supervised learning - and then, using these models, the algorithms predict the outcomes for the unlabeled data, essentially labeling them. The full dataset- labeled and pseudo-labeled data - is now available which is used to train models.

Semi-supervised learning occurs based on a few assumptions.

1. Continuity assumption

Continuity assumption means that data points that are closer to each other are more likely to have similar output labels.

2. Cluster assumption

According to a paper titled 'A survey on semi-supervised learning' (Van Engelen, J.E., Hoos, H.H., 2020) published in the journal Machine Learning, Cluster assumption means that the data points in one cluster are likely to be of the same class and, thus, have the same output label. Data points in different clusters would then have different output labels.

3. Manifold assumption

Manifold assumption means that the data lies on a lower dimension than the input space.

What are the types of semi-supervised learning?

1. Transductive learning

The reasoning learned is not adopted to data that is not present.

This means, for data points newly added, the learnings cannot be generalized, and predictions cannot be made.

2. Inductive learning

The model trained here can be used to predict labels of a dataset that is not present in the initial testing data too.

What are the advantages of semi-supervised learning?

  1. Supervised learning, which relies on labeled data is tricky, because that huge an amount of labeled data does not exist in most cases. Semi-supervised learning was introduced to handle this situation.
  2. The cost of labeling data is very high because human intervention is necessary to do it manually. That is a process that involves many tedious hours of work. This is especially if one is dealing with large amounts of data.
  3. Semi-supervised learning is used to effectively cut down on the time and energy spent.

Applications of semi-supervised learning

Semi-supervised learning is used for speech analysis because manual leveling of audio files is a very intensive task. Google and other search engines use variants of semi-supervised learning to optimize searches according to relevance based on semi-supervised algorithms.

Novel applications are being pushed by disruptive startups like Tooliqa.

Using semi-supervised learning to drive AI outputs like creating accurate 3D models and taking over the tedious task of looking over every project’s progress from man-to-machine, these new companies are pushing the boundaries of machine learning applications.

The breakthroughs we are witnessing and the ones which are underway is charting a new course for future and the journey gets exciting with each passing day.

Also read: Modelling using Unlabeled Data (

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Quick queries for this insight

What is the difference between transductive and inductive learning?
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Transductive learning is a form of learning where the learner draws specific conclusions from a given set of data or observations. In contrast, inductive learning is a form of learning where the learner generalizes from a given set of data or observations. Transductive learning is often used to solve specific problems or to make specific predictions, while inductive learning is often used to develop new theories or to extend existing knowledge.

What is reinforcement learning?
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Reinforcement learning is a type of machine learning that enables agents to learn from their environment by trial and error. With reinforcement learning, an agent is able to take actions in an environment and receive feedback in the form of rewards or punishments. Over time, the agent learns to associate certain actions with positive outcomes and repeat those behaviors. This type of learning has been shown to be highly effective in tasks such as video game playing and robotics. In recent years, reinforcement learning has also been used to develop autonomous vehicles. One of the benefits of reinforcement learning is that it can be used in environments where there is a large amount of data available. This allows agents to learn from experience and improve their decision-making over time. Additionally, reinforcement learning can be used in real-time settings, where an agent needs to make decisions quickly and cannot wait for human feedback.

What is the difference between continuity and cluster assumption?
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The continuity and cluster assumptions are two important concepts in statistics and semi-supervised learning. The continuity assumption states that a given statistic (such as the mean) is continuous, meaning that it can take on any value within a certain range. The cluster assumption, on the other hand, states that a given statistic is clustered, meaning that it takes on a limited number of values within a certain range. The two assumptions are closely related, but there is an important distinction between them. The continuity assumption is more general, stating simply that a given statistic is continuous. The cluster assumption is more specific, stating that a given statistic is clustered. In other words, the cluster assumption implies the continuity assumption, but not vice versa.

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