Interpretable Multiple-Kernel Prototype Learning for Discriminative Representation and Feature Selection

TL;DR

This paper introduces IMKPL, a novel interpretable multiple-kernel prototype learning method that enhances class discrimination and feature selection while maintaining interpretability, outperforming existing approaches across various benchmarks.

Contribution

IMKPL is a new method that constructs interpretable prototypes in kernel spaces, balancing interpretability and discriminative power, with an embedded feature selection mechanism.

Findings

IMKPL outperforms state-of-the-art methods in interpretability and discrimination.

The method effectively selects discriminative features in kernel spaces.

IMKPL demonstrates superior results across multiple benchmark datasets.

Abstract

Prototype-based methods are of the particular interest for domain specialists and practitioners as they summarize a dataset by a small set of representatives. Therefore, in a classification setting, interpretability of the prototypes is as significant as the prediction accuracy of the algorithm. Nevertheless, the state-of-the-art methods make inefficient trade-offs between these concerns by sacrificing one in favor of the other, especially if the given data has a kernel-based representation. In this paper, we propose a novel interpretable multiple-kernel prototype learning (IMKPL) to construct highly interpretable prototypes in the feature space, which are also efficient for the discriminative representation of the data. Our method focuses on the local discrimination of the classes in the feature space and shaping the prototypes based on condensed class-homogeneous neighborhoods of data. Besides, IMKPL learns a combined embedding in the feature space in which the above objectives are better fulfilled. When the base kernels coincide with the data dimensions, this embedding results in a discriminative features selection. We evaluate IMKPL on several benchmarks from different domains which demonstrate its superiority to the related state-of-the-art methods regarding both interpretability and discriminative representation.