# Rare geometries: revealing rare categories via dimension-driven statistics

## TL;DR

This paper introduces a new supervised learning algorithm that uses a dimension-driven statistic called the kappa-profile to detect rare classes in data, requiring few labeled examples and handling both separable and non-separable cases.

## Contribution

The paper proposes a novel dimension-based statistic and a classification algorithm specifically designed for rare-category detection with minimal labeled data.

## Key findings

- Effective detection of rare classes with few labels
- Invariant to translation, working on separable and non-separable classes
- Demonstrates improved performance over existing methods

## Abstract

In many situations, classes of data points of primary interest also happen to be those that are least numerous. A well-known example is detection of fraudulent transactions among the collection of all financial transactions, the vast majority of which are legitimate. These types of problems fall under the label of `rare-category detection.' There are two challenging aspects of these problems. The first is a general lack of labeled examples of the rare class and the second is the potential non-separability of the rare class from the majority (in terms of available features). Statistics related to the geometry of the rare class (such as its intrinsic dimension) can be significantly different from those for the majority class, reflecting the different dynamics driving variation in the different classes. In this paper we present a new supervised learning algorithm that uses a dimension-driven statistic, called the kappa-profile, to classify whether unlabeled points belong to a rare class. Our algorithm requires very few labeled examples and is invariant with respect to translation so that it performs equivalently on both separable and non-separable classes.