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The potential of unlabeled data: why are they so important for machine learning?
With the rise of large language models, the importance of unlabeled data in machine learning has increased dramatically. This model is called weakly supervised learning, or semi-supervised learning. Its core is to combine a small amount of human-labeled data with a large amount of unlabeled data for training. This means that only a portion of the data's output values are labeled, while the remaining data is unlabeled or imprecisely labeled. This approach provides an efficient solution to make full use of rich unlabeled data when labeling is expensive and time-consuming.
In the field of modern machine learning, the cost of obtaining annotated data is often extremely high, making it impractical to have large-scale complete annotated data sets.
When it comes to labeling data, many academics and engineers immediately think of the high expense involved in the labeling process. This process may require specialized personnel, such as transcribing audio clips or conducting physical experiments to identify specific phenomena. Therefore, semi-supervised learning is not only theoretically interesting, but also actually provides feasible solutions to various problems. This becomes a powerful tool to bridge between labeled and unlabeled data.
The technology of semi-supervised learning assumes a certain correlation, allowing it to utilize large amounts of unlabeled data to significantly improve classification performance.
The technique of semi-supervised learning assumes the ability to extract meaningful information from the underlying distribution of the data. These techniques include continuity assumptions, clustering assumptions, and manifold assumptions. These assumptions help learn structure from unlabeled data, for example, when data points are close to each other, they are more likely to have the same label. Additionally, data often forms discrete clusters, so points within the same cluster may share labels. Under this assumption, semi-supervised learning can learn the intrinsic characteristics of the data more efficiently.
The manifold hypothesis states that data are often located on low-dimensional manifolds. This view allows the learning process to avoid the curse of dimensionality.
The history of semi-supervised learning can be traced back to the self-training method in the 1960s. Later, in the 1970s, Vladimir Vapnik formally introduced the framework of conductive learning and began to explore induced learning using generative models. These methods have begun to become a hot spot in theoretical research and promote the development of machine learning.
In practical applications, various methods are intertwined, forming a relatively complex ecosystem. The generative model first estimates the distribution of data under different categories, which enables the model to learn effectively even when there is insufficient annotated data. Similarly, low-density separation methods achieve the purpose of separating labeled data from unlabeled data by drawing boundaries in areas where data points are sparse.
In this series of techniques, Laplacian regularization uses graph representation to perform data learning. These graphs connect each labeled and unlabeled sample through similarity, emphasize the internal connection of the data through the structure of the graph, and further use unlabeled data to drive the learning process.
Theoretically, semi-supervised learning is a model that simulates the human learning process, which makes it attractive and practical.
To summarize, the rise of weakly supervised learning is precisely to solve the challenge of scarcity of labeled data and demonstrates the huge potential of unlabeled data. With the rapid growth of data and the continuous evolution of machine learning technology, we may need to rethink: How should we better utilize the potential of unlabeled data in future research?
