Semi-Supervised Machine Learning
Semi-supervised learning (SSL) is a machine learning approach that combines the small amount of labeled data with a large amount of unlabeled data to build more robust models than supervised or unsupervised learnings alone. This approach is useful when labeled-data is expensive or time-consuming.
Types of Semi-Supervised Techniques
Here’s a breakdown of the types of semi-supervised learning techniques, with applications.
Self-Training
Trains the model on labeled data and then uses this model to predict labels on unlabeled data, and confident predictions are added to the labeled dataset. This process is repeated iteratively.
Applications include, image recognition and natural language processing.
Co-Training
Uses two models with different features or views of data and each model labels unlabeled data for each other, allowing them to learn from each other iteratively.
Used in a tasks where data can be split into two views, like text (content vs metadata) or webpage classification.
Graph-Based Method
Uses graphs to represent relationship between data points(nodes) and connect similar points (edges). Labeled and unlabeled data are connected in a graph, and labels propagate across edges to predict labels for unlabeled data.
Applications of graph-based methods are social network analysis, and image segmentation.
Generative Models
Models like Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) learn the distribution of the data to generate new samples. When combined with labeled data, these models help improve classification by creating additional synthetic data points or by learning better feature representations.
Used in data augmentation, image generation, and language modeling.
Applications of Semi-Supervised ML
- Image Recognition: Semi-supervised learning is frequently used to classify images where labelling every image is costly. SSL helps models generalize better with fewer labeled images.
- Natural Language Processing (NLP): Tasks such as sentiment analysis, text classification, and named-entity recognition, only a small portion of data might be labeled. Semi-supervised learnings leverages vast amounts of unlabeled text available online to improve model performance.
- Speech Recognition: SSL is used in speech-to-text models where only some audio clips are labeled with transcriptions, making it feasible to leverage large-scale unlabeled audio data to improve model accuracy.
- Fraud detection: Financial fraud detection can benefit from SSL, as labeling transactions as fraud or non-fraud is difficult and often only available for a small portion of the dataset.
- Webpage Classification and Content Recommendation: Content recommendation systems benefit from SSL by learning from both labeled and unlabeled browsing behavior. Similarly, SSL helps in categorizing webpages where labeled examples are sparse.
Thus semi-supervised learning methods strike a balance between supervised and unsupervised learning, offering a practical solution when labeled data is limited but unlabeled data is abundant. Each method has unique applications, allowing flexibility based on the problem's data availability and requirements.