Semi-Supervised Machine Learning, New Algorithms

While supervised, unsupervised, and reinforcement machine learning are the three most common forms of machine learning algorithms that are utilized by software developers and engineers around the world today, there is another form of machine learning that has also gained traction in recent years, semi-supervised machine learning. As the name suggests, semi-supervised machine learning combines aspects of both supervised and unsupervised machine learning to create new algorithms. Due to the difficulties in obtaining the labeled datasets that supervised and reinforcement learning algorithms require to function, as well as the time that it can take to compile and manually label the data within said datasets, software developers can leverage semi-supervised machine learning algorithms to gain insight and knowledge from a particular set of data in a more cost-effective and efficient way.

How does semi-supervised machine learning work?

Semi-supervised machine learning functions on the basis of pseudo labeling. Pseudo-labeling is defined as the process of using labeled data within a dataset to predict labels for the unlabeled data within the same dataset. The goal of pseudo-labeling is to create a high-performance machine learning model while using the least amount of labeled data possible. To begin the process, a software developer will first train their model using a batch of labeled data, in a manner similar to that of supervised learning. Next, the software developer will then use the trained model to predict labels for the unlabeled data within the dataset, hence the name pseudo-labeling. After the unlabeled data within the dataset has been labeled through pseudo-labeling, this data will then be combined with the labeled data that was initially used to train the model. This use of pseudo-labeling is also referred to as transductive learning.

Through the combination of both the labeled and pseudo-labeled data, a software developer will have access to a large amount of labeled data in a fraction of the time it would have taken to compile such data through supervised learning or manual efforts. More importantly, however, the pseudo-labeled data can be used to ensure that the semi-supervised machine learning model is as accurate as possible. To illustrate this point further, consider a software developer looking to create a machine learning algorithm that can automatically detect the faces of cats. The software developer creating this algorithm might have a dataset of 10,000 different images of cats, of which 1,000 have been manually labeled. However, the software developer in question may not have the time and resources needed to label the other 9,000 images.

To this point, using the process of transductive learning through the utilization and implementation of pseudo-labeled data, the software developer can use the 1,000 images that have already been labeled to effectively predict labels for the other 9,000 images that have yet to be labeled. As such, the software developer will initially train their model on the 1,000 labeled images and then combine the labeled images with the pseudo-labeled images to finish training their model. In this way, the software developer can ensure that they are fully optimizing the data within their dataset, while also ensuring that the costs and effort needed to complete their machine learning model do not exceed the practicality or feasibility of creating such a model in the first place.

What are the advantages of semi-supervised machine learning?

While the benefits and advantages of semi-supervised machine learning, when compared with supervised learning, have already been discussed, semi-supervised learning models can also be preferable to unsupervised and reinforcement learning models under certain conditions. For example, while unsupervised learning models function on the basis of unlabeled data, the real-world applications of such algorithms can prove to be extremely limited in practice. Alternatively, reinforcement learning algorithms are not an ideal solution for solving simple problems, as the technique requires an immense amount of data and computational power to effectively function. As such, some software developers and engineers may face situations where supervised, unsupervised, or reinforcement learning algorithms are not viable options for the task at hand, whether it be from a technical or business standpoint.

As machine learning algorithms continue to advance and develop, new techniques will continue to be formulated for the purpose of creating the most accurate and efficient machine learning models possible. Semi-supervised machine learning represents one such technique that can combine other forms of machine learning, allowing software engineers to find solutions to problems that may have been difficult to solve using other forms of machine learning. With this being said, the decision on whether to utilize a supervised, semi-supervised, unsupervised, or reinforcement learning model will hinge on the specific needs or desires of the software developer that is creating the algorithm, as well as the time and resources that the developer in question will need to solve the problem.