Understanding What is Semi-Supervised Learning: Bridging the Gap Between Supervised & Unsupervised

Machine learning is a strong sector of Artificial Intelligence that allows computers to learn autonomously without any direct programming. Rather than grasping a strict set of guidelines, Machine Learning algorithms can examine data, recognize patterns, and generate predictions. The progression from supervised to unsupervised learning signifies a transition from a machine learning context where the data is completely tagged with known results to one where the data lacks labels, compelling the model to identify patterns and connections in the data without direct instruction.

Semi-supervised learning appears as an innovative hybrid technique, merging supervised and unsupervised methods by incorporating both labeled and unlabeled data to produce stronger and more effective models. The write-up below discusses What is Semi Supervised learning and its insight in detail.

What is a Semi-Supervised Learning Algorithm?

Semi-supervised learning is a subset of machine learning that merges supervised and unsupervised learning techniques by incorporating both labeled and unlabeled data to train Artificial Intelligence models for classification and regression usage.

Examples of Semi-Supervised Learning

The volume of data in the universe is growing at its peak, whereas the number of human hours available for labeling it is rising at a significantly slower rate. This creates an issue since there are countless locations where we aim to utilize machine learning. Semi-supervised learning allows a potential resolution to this issue, and in the upcoming sections, we will outline real-life examples of semi-supervised learning.

• Detecting instances of fraud: In the finance sector, semi-supervised learning can be applied to develop systems for detecting cases of fraud or extortion. Instead of manually labeling thousands of separate instances, engineers can begin with a limited number of labeled examples and then use one of the semi-supervised learning methods mentioned above.
• Categorizing online content: The web is vast, and new sites are created constantly. To provide effective search results, it is essential to categorize large volumes of web content, achievable through semi-supervised learning.
• Examining audio and visuals: This may be the most well-known application of semi-supervised learning. When audio or image files are created, they frequently lack labels, making their use in machine learning challenging. Starting with a limited amount of human-labeled data, this issue can be addressed.

Assumptions of Semi-Supervised Learning Algorithms

Assumptions are necessary for semi-supervised learning to function effectively. These assumptions are empirical mathematical insights that validate otherwise random decisions regarding the method used. The two assumptions we will talk about are the smoothness and clustering assumptions.

• Smoothness Assumption: This premise asserts that two data points x1 and x2 within a high-density area (from the same cluster) are near each other, thus the related output labels y1 and y2 should also be similar. Conversely, when the data points are in a low-density area, their outputs do not have to be similar.
• Cluster Assumption: The cluster assumption posits that data points within the same cluster are probably of the same class. Unlabeled data can help to more precisely determine the boundary of each cluster when employing clustering algorithms. Moreover, the labeled data instances ought to be utilized to designate a category for every cluster.
• Manifold Assumption: This premise underlies various semi-supervised learning techniques; it asserts that in higher-dimensional input space, multiple lower-dimensional manifolds contain all data points, and data points sharing the same label reside on the same manifold.

Types of Semi-Supervised Learning Algorithms

• Low-Density Separation

The assumption of Low-Density Separation indicates that the decision boundary ought to be positioned in the area of low density. Take digit recognition as an example; one aims to differentiate a handwritten digit “0” from a digit “1”. A sample point drawn precisely from the decision boundary will fall between 0 and 1, probably resembling a digit that looks like a highly stretched zero. However, the chance that a person authored this "strange" digit is quite low.

• Generative Models

Consequently, generative models became a crucial method in semi-supervised learning. The goal is to define the distribution of data. Once this distribution is understood, the model can generate data and assess its probability of fitting into a specific class. This idea frequently employs methods like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs).

• Graph-Based Methods

Graph-based semi-supervised learning methods utilize a graph to illustrate the connections among the data, which acts as a crucial preprocessing stage for generating new features using local neighborhood insights. At its essence, the fundamental concept is to depict data points as nodes and their interrelations through links. We can utilize these connections to transfer the label information from some labeled nodes to all their unlabeled node acquaintances based on the strength of their relationships within the graph.

How Does Semi-Supervised Learning Algorithm Work?

Integration of Labeled and Unlabeled Data

In Semi-Supervised Learning (SSL), a combination of labeled and unlabeled data is employed by initially training a model with a limited labeled dataset, followed by using that model to assign labels to the extensive unlabeled dataset. Subsequently, the model is refined by retraining on both the labeled and pseudo-labeled datasets. 

This process enables the model to utilize the structure and features found in the unlabeled data, thereby enhancing its accuracy and ability to generalize; fundamentally, the labeled data offers a primary learning direction, while the unlabeled data aids in honing the model's comprehension of the data distribution.

Techniques

• Self-Training: Self-training is an easy and widely used semi-supervised learning technique. The fundamental concept is simple: initially train using the labeled data and subsequently forecast labels for the unlabeled data.
• Co-Training: Co-Training is a semi-supervised learning algorithm based on features that are particularly effective when each sample can be represented from different perspectives. Each perspective reveals a different aspect of the same information.
• Graph-Based Methods: Graph-based semi-supervised learning methods utilize a graph to depict the connections among the data, acting as a crucial preprocessing phase for generating new features from local neighborhood data.

Application of Semi-Supervised Learning

• NLP (Natural Language Processing)

Natural Language Processing (NLP) operates with large datasets that contain no labels through cooperation with smaller labeled datasets for machine learning training. Text analysis tools perform better through the combination of contextual information extracted from unlabeled data with limited labeled data labels which enables them to understand and process texts in sentiment analysis processes and named entity recognition systems and text summarization operations.

• Image Recognition

A widely used application of semi-supervised learning models is the examination of images and audio. Typically, this type of data remains unlabeled. Rather than categorizing each image or audio file for a particular domain over many days or months, a tiny portion of the data can be tagged by knowledgeable humans. After classifying this small data sample, you can readily categorize the remaining data using the trained algorithm.

• Web Content Classification

With billions of websites showcasing various types of content, categorization would require a massive team of human resources to organize information on web pages by attaching relevant labels. The different forms of semi-supervised learning are utilized to label web content and categorize it appropriately to enhance user experience. Numerous search engines, such as Google, utilize SSL in their ranking algorithms to improve comprehension of human language and the pertinence of potential search outcomes to user queries. Through SSL, Google Search locates content that is most pertinent to a specific user inquiry.

• Medical Diagnosis

Medical diagnosis in semi-supervised learning" pertains to employing a semi-supervised learning method to develop a model that aids in identifying medical issues, utilizing a limited set of labeled medical data together with a vast collection of unlabeled data to enhance diagnostic accuracy, particularly in scenarios where labeled data is limited or costly to obtain; this enables the model to detect patterns from the unlabeled data while being anchored by the precise labels given in a smaller group.

Advantages and Disadvantages of Semi-Supervised Learning Algorithm

The main benefits of semi-supervised learning stand out compared to standard machine learning while it simultaneously carries several disadvantages. The following points analyze both the benefits and drawbacks of semi-supervised learning.

Benefits of Semi-Supervised Learning:

• Extensive quantities of unlabeled information: Executing tasks with untagged information becomes highly beneficial as it reduces both the price and time needed for labeled data acquisition. Semi-supervised learning performs efficiently through the processing of enormous unlabelled datasets available in the wild which produces superior training capabilities.
• More effective models: There exist more effective models from semi-supervised learning than supervised approaches that work with limited labeled datasets. Unlabeled data provides additional value through its organizational structure to guide the model system toward higher performance levels.
• Lowers Labeling Expenses: The process of assigning classifications to data creates major difficulties during machine learning projects. This data learning approach requires limited labeled examples which makes the process more affordable while reducing manual work expenses.
• Handles Diverse Modalities of Data: The ability of several semi-supervised algorithms enables their successful use of information from different data sources including images alongside text and sensor readings. The diverse characteristics of this approach make it suitable for handling numerous machine-learning problems.
• Could provide valuable patterns: The hidden patterns within unlabeled data remain intact whereas supervised learning would usually overlook them. Analyzing insignificantly out-of-date data together with marked-up provides detection of hidden patterns that in turn can give new knowledge and improve the results of the model.

Disadvantages Of Semi-Supervised Learning:

• Opting for the Appropriate Algorithm: Various semi-supervised techniques possess distinct positive aspects and drawbacks, making it hard to pick the right one for a specific data and task involved. Opting an inappropriate method can result in less-than-ideal performance or even obstruct outcomes.
• Sensitivity to Label Noise: If the data without labels has mistakes or deceptive information (label noise), it can adversely affect the model's learning experience and result in erroneous predictions.
• Computational Complexity: Certain semi-supervised techniques, especially those that utilize intricate graph frameworks or generative models, may incur high computational costs, particularly when dealing with large datasets. Effective implementations and hardware optimization are frequently required.
• Restricted Theoretical Assurances: In contrast to supervised learning, which has robust theoretical underpinnings, semi-supervised learning approaches frequently do not have solid theoretical guarantees regarding their efficacy. This complicates the ability to anticipate their actions and evaluate their constraints.

Also Read: Exploring the Scope of Machine Learning: Trends, Applications, and Future Opportunities

Difference Between Supervised, Unsupervised and Semi-Supervised Learning Algorithms

Below is the comparison between Supervised, Unsupervised, and Semi-Supervised Learning Algorithms under various parameters. 

Various approaches with a few example algorithms:

Various approaches with a few examples uses:

Challenges using each approach

How upGrad Can Help You?

Machine learning is continuously advancing, influencing the future of technology and significantly affecting our lives. UpGrad uses semi-supervised learning and provides well-designed and structured machine learning courses and training sessions that will help you understand the theoretical knowledge and hands-on experience to implement your projects using semi-supervised learning concepts. This enables them to use a powerful abundance of unlabeled data along with small volumes of annotated datasets to create more robust models for learning algorithms, especially when obtaining labeled data is scarce or costly.

UpGrad often collaborates with experts in the field to share how to use semi-supervised learning techniques in various industries. Supervised projects allow students to work with real data by applying semi-supervised learning algorithms.

Here are some relevant courses you can check out:

• Master of Science in Machine Learning & AI
• Executive Diploma in Machine Learning and AI with IIIT-B
• Post Graduate Certificate in Machine Learning & Deep Learning (Executive)
• Post Graduate Certificate in Machine Learning & NLP (Executive)

For personalized counseling and guidance contact our upGrad team. For more details, you can also visit your nearest upGrad offline center.

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