Geometry-Aware Bayesian Quantification via Compositional Data Analysis
For practitioners needing robust class prevalence estimation under label shift, this work offers a principled geometric improvement over existing KDE methods, though gains are incremental.
The paper addresses label shift adaptation by improving KDE-based quantification with geometry-aware modeling on the probability simplex. The proposed method achieves competitive performance with state-of-the-art quantifiers across 42 datasets, often outperforming standard KDE baselines.
Accurately estimating the unknown target label distribution is the critical first step for adapting to label shift. This task, widely known as quantification or class prevalence estimation, has recently seen significant advances through continuous KDE-based methods which model the density of multiclass classifier posteriors. Posterior vectors might be regarded as compositional data, since they lie on the probability simplex. However, existing KDE-based quantifiers typically rely on Euclidean Gaussian kernels, which ignore simplex geometry and incorrectly assign probability mass outside its boundaries. We introduce a geometry-aware KDE model for multiclass quantification based on log-ratio representations and Aitchison geometry, together with a shrinkage regularization that improves robustness near the simplex boundary. Combined with a maximum-likelihood interpretation of KDE-based quantification, we derive both point-estimation and Bayesian inference procedures for class prevalences. Experiments on 42 datasets across tabular, text, and image domains show that the proposed method is competitive with state-of-the-art quantifiers, often improving over standard KDE-based baselines, while also yielding strong results among Bayesian quantification methods.