Semi Supervised Learning in ML – Explain With Example

Semi supervised learning is a machine learning method that combines labeled and unlabeled data. Unlike supervised learning, which requires large amounts of labeled data, and unsupervised learning, which relies solely on unlabeled data, semi-supervised learning (SSL) utilizes a combination of both. It is helpful when labelling data is expensive or hard but there’s plenty of unlabeled data available. SSL is used in many areas like image recognition, language processing, and medical diagnosis. In this article, we will explain how SSL works, look at different algorithms and techniques, and discuss its benefits and real-life examples.

Introduction to Semi Supervised Learning

Semi-supervised learning is a machine learning method that sits between supervised and unsupervised learning. Supervised learning needs a lot of labeled data, which can be costly and take time. While unsupervised learning only uses data without labels. Semi-supervised learning finds a middle ground by using a small amount of labeled data as well as a larger amount of unlabeled data to train models.

This approach is especially helpful when labeling data is hard, expensive, or slow, but there’s plenty of unlabeled data available. It’s commonly used in areas like image recognition, language processing, and medical diagnosis.

How Does Semi Supervised Learning Work?

In semi-supervised learning, we start with a small set of labeled data to train a model. After that, we use this model to predict labels for a much larger set of unlabeled data. After that, we combine the labeled data with the newly labeled data to make our model more accurate and reliable.

Steps in Semi Supervised Learning

• Initial Training: The model starts by learning from a small set of labeled data. By giving it a good base to work with.
• Pseudo-Labeling: After the model has learned enough, it uses what it knows to guess labels for the unlabeled data.
• Refinement: The model is then improved by combining the labeled data and the newly guessed labels, making it more accurate.

The goal is to use both labeled and unlabeled data effectively to create a better model than you could with just a small amount of labeled data.

Semi Supervised Learning Algorithms List

There are quite a few semi-supervised learning algorithms out there, and each one is meant to work with different kinds of information and tasks. Here are a few popular algorithms used in semi-supervised learning.

1. Self-Training: In self-training, the model learns from labeled data first. Then, it uses what it has learned to label the unlabeled data. These new labels are added to the training set, and the process repeats.
2. Co-Training: In co-training, two models are trained on different parts of the data. Each model uses its predictions to label data for the other model. This also helps reduce errors and makes the models better.
3. Graph-Based Algorithms: Graph-based algorithms treat data points as nodes in a graph, with similar points connected by edges. The labels from labeled nodes are spread to nearby unlabeled nodes. Which also helps classify the unlabeled data more effectively.
4. Generative Models: Generative models assume that both labeled and unlabeled data come from the same source. By modeling this source, they can make better guesses when labeling the unlabeled data.
5. Low-Density Separation: Low-density separation algorithms, like Transductive Support Vector Machines (TSVMs), aim to find decision boundaries in areas where data is sparse. Moreover, this helps the model generalize better to new, unseen data.

Semi Supervised Learning Techniques

Various methods have been created to help with learning from incomplete data and dealing with errors. Here are some of the most commonly used methods:

1. Consistency Regularization

This method ensures that the model gives consistent predictions even when the input data is slightly changed. By doing this, the model becomes better at handling new, unseen data.

2. Entropy Minimization

Entropy minimization helps the model make more confident predictions by reducing uncertainty in the labels it assigns to unlabeled data.

3. Virtual Adversarial Training (VAT)

VAT adds small changes to the data to make the model face tougher challenges. This helps the model become more accurate and resistant to errors.

4. Label Propagation

In label propagation, the labels from labeled data points are passed on to nearby unlabeled points based on how similar they are. This method is often used in graph-based semi-supervised learning.

What is an Example of a Semi-Supervised Model?

A good example of semi-supervised learning is Google Photos. It starts by training the model with labeled images. Once trained, it can label new images by comparing them to the labeled ones. Over time, as more users label photos, the system improves its accuracy.

Another example is speech recognition systems. These systems begin with a small amount of labeled speech data and then use large amounts of unlabeled audio recordings to improve how well they transcribe speech.

Semi Supervised Learning Advantage

Semi-supervised learning offers many advantages compared to supervised and unsupervised learning. Let’s take a look at some of these benefits.

• Efficient with Less-Labeled Data: Semi-supervised learning uses a lot of unlabeled data well, which means you need less expensive labeled data.
• Better Model Performance: Using labeled and unlabeled data often makes the model work better than one that only uses labeled data, especially if you have only a little labeled data.
• Cost-Effective
  Labeling data is hard and expensive, especially in fields like healthcare. Semi-supervised learning helps lower these costs while still keeping the model effective.
• Flexible
  Semi-supervised learning works well in many areas, like image recognition, language processing, and speech recognition, making it useful for many types of machine-learning tasks.

What is Graph Based Semi Supervised Learning?

One common graph-based semi-supervised learning method creates a graph. Where each data point is a node, and edges show how similar the points are. Labeled data points help spread their labels to nearby unlabeled points, which also helps classify them.

Example of Graph-Based Semi-Supervised Learning: In a medical diagnosis example, patients are shown as nodes and edges represent how similar their symptoms are. Diagnosed patients (with labels) pass their labels to undiagnosed patients. It also, helps the model predict diseases for new patients more accurately.

Semi Supervised Classification with Graph Convolutional Networks

Graph Convolutional Networks (GCNs) are a really useful tool for classifying things when you don’t have all the information. They’re like a more advanced version of the technology used in facial recognition, but they can also work with more complicated types of data, like social networks or the internet.

How GCNs Work?

GCNs work by gathering information from a node’s neighbors step by step to learn more about it. This information is then used to classify all the nodes in the graph, including those without labels. GCNs are very useful for things like social network analysis, recommendation systems, and predicting molecular properties.

Also Read: Unsupervised vs Supervised Machine Learning – Explained in Detail

Conclusion

In conclusion, semi supervised learning is a great way to use both labeled and unlabeled data to improve machine learning models. It is beneficial when labeling data is expensive or hard to get. However, there is a lot of unlabeled data available. SSL makes models better and cheaper to train by using methods like self-training, co-training, and graph-based techniques. With new advances like Graph Convolutional Networks (GCNs) and Virtual Adversarial Training (VAT). SSL is becoming a powerful tool in many fields, from healthcare to image recognition. As more data becomes available, SSL will keep being important for building strong and effective AI models.

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