Semi-supervised Learning

Machine learning has turned out to be exceptionally effective in classifying photos and other unstructured data, a task that traditional rule-based software struggles with. Machine learning models, on the other hand, must be trained on a large number of annotated examples before they can perform classification tasks.

Data annotation is a time-consuming and labor-intensive procedure that requires humans to go over each training example one by one and provide the appropriate label.

Indeed, data annotation is such an important aspect of machine learning that the technology’s expanding popularity has created a vast industry for labeled data.

Luckily, you don’t have to label all of your training instances for some classification tasks.

  • You can utilize semi-supervised learning to partially automate the data labeling process.

Supervised, unsupervised, and semi-supervised learning

  • Supervised learning in machine learning ground truth for your AI model must be specified during training. Facial identification, image classification, customer churn prediction, spam detection, and sales forecasting are examples of problems that are being solved with supervised learning methods.
  • Unsupervised learning – is used when the ground truth is unknown and machine learning models are used to uncover meaningful patterns. Anomaly detection in network traffic, customer segmentation, and content recommendation are all examples of unsupervised learning.
  • Semi-supervised learning in machine learning sits in the middle of the two. It addresses classification problems, thus you’ll need a supervised learning algorithm to finish the job. However, you also want to train your model without labeling every single training example, which semi-supervised algorithms can help you with.

Application of Semi-supervised Learning

An example of semi-supervised learning is merging clustering and classification algorithms. Clustering algorithms are unsupervised machine learning approaches for grouping data based on similarity. We’ll use the clustering approach to locate the most relevant samples in our data collection. We can then label them and use them to train our categorization supervised machine learning model.

Let’s say we want to train a machine learning model to categorize handwritten digits, but we only have a large dataset of unlabeled digit photos. We won’t be able to annotate every sample, therefore we’ll have to rely on semi-supervised learning to build your AI model.

To begin, we group our samples using k-means clustering. K-means is an unsupervised learning technique that works quickly and efficiently without the need for labels. By evaluating the distance between our samples’ features, K-means calculates their similarity. Every pixel in our handwritten numbers will be considered a feature, therefore a 20x 20 image will include 400 features.

  • You must indicate how many clusters you want to divide your data into when training the k-means model. For the k-means machine learning model, the number of clusters you choose should be bigger than the number of classes.

Our data will be separated into clusters when the k-means algorithm has been trained. A centroid is a set of numbers that represents the average of all features in a cluster in a k-means model. In each cluster, we select the most representative image, which also happens to be the image closest to the centroid.

We can now identify these photos and use them to train our second machine learning model, which may be a logistic regression model or another supervised learning engine.

It may appear that training a machine learning model on a few instances rather than hundreds of photos is a bad idea. The machine learning model’s conclusion will be exceptional since the k-means model identified a few photographs that were most representative of the distributions of our training data set.

We can spread the same label to other samples in the same cluster after we label the representative samples of each cluster. With just a few lines of code, we can annotate thousands of training samples. This will help our machine learning model perform even better.

Conclusion

However, all supervised learning tasks are not suitable for semi-supervised learning. Your classes should be able to be separated using clustering techniques, just as the handwritten digits. Alternatively, you must have enough labeled examples, and those examples must cover a fair representation of the problem space’s data production process.

Unfortunately, many real-world applications fall into that group, therefore data labeling tasks are unlikely to disappear very soon.

Semi-supervised learning, on the other hand, has a lot of applications in domains where data labeling may be automated, such as simple image classification and document categorization.

  • Semi-supervised learning is a fantastic approach that can be quite useful if you know when to employ it.