proxylabeling

Proxylabeling is a technique used in machine learning and artificial intelligence to assign labels to data points based on their proximity to other labeled data points. The core idea is that if two data points are similar or close to each other in a feature space, they are likely to share the same label. This is particularly useful in scenarios where obtaining labels for all data is expensive or time-consuming.

The process typically involves an initial set of labeled data, often called seeds. Then, an algorithm analyzes the relationships between these seeds and the unlabeled data. Various similarity measures can be employed, such as Euclidean distance, cosine similarity, or graph-based methods. The algorithm propagates labels from the seeds to nearby unlabeled points. For instance, a data point that is very close to a labeled data point might inherit that label with high confidence. Points further away might receive labels with lower confidence or through multiple propagation steps.

Proxylabeling is often integrated into semi-supervised learning frameworks. It can be used to bootstrap the learning process, improving the performance of models that rely on labeled data. Different variations exist, including methods that consider the density of data points or the structure of the underlying data distribution. The effectiveness of proxylabeling depends on the quality of the initial seeds, the chosen similarity metric, and the distribution of the data itself.