Understanding density-weighted sampling methods
Density-weighted methods are approaches that aim to carefully choose points that accurately represent the densities of their respective local neighborhoods. By doing so, these methods prioritize the labeling of diverse cluster centers, ensuring a comprehensive and inclusive representation of the data.
Density-weighted techniques are highly beneficial and effective when it comes to querying points. These techniques utilize a clever combination of an informativeness measure and a density weight. An informativeness measure provides a score of how useful a data point would be for improving the model if we queried its label. Higher informativeness indicates the point is more valuable to label and add to the training set. In this chapter, we have explored several informativeness measures, such as uncertainty and disagreement. In density-weighted methods, the informativeness score is combined with a density weight to ensure we select representative...
