One Line To Rule Them All: Generating LO-Shot Soft-Label Prototypes

TL;DR

This paper introduces a novel method for generating soft-label prototypes that effectively represent large datasets with fewer prototypes, enabling efficient classification even with imbalanced and complex data.

Contribution

The paper presents a new modular approach for creating soft-label prototypical lines and a hierarchical k-NN classifier that maintains accuracy with fewer prototypes, even in challenging datasets.

Findings

High classification accuracy with fewer prototypes

Effective in imbalanced and difficult datasets

Reduces computational costs significantly

Abstract

Increasingly large datasets are rapidly driving up the computational costs of machine learning. Prototype generation methods aim to create a small set of synthetic observations that accurately represent a training dataset but greatly reduce the computational cost of learning from it. Assigning soft labels to prototypes can allow increasingly small sets of prototypes to accurately represent the original training dataset. Although foundational work on `less than one'-shot learning has proven the theoretical plausibility of learning with fewer than one observation per class, developing practical algorithms for generating such prototypes remains an unexplored territory. We propose a novel, modular method for generating soft-label prototypical lines that still maintains representational accuracy even when there are fewer prototypes than the number of classes in the data. In addition, we propose the Hierarchical Soft-Label Prototype k-Nearest Neighbor classification algorithm based on these prototypical lines. We show that our method maintains high classification accuracy while greatly reducing the number of prototypes required to represent a dataset, even when working with severely imbalanced and difficult data. Our code is available at https://github.com/ilia10000/SLkNN.