Low-dimensional Interpretable Kernels with Conic Discriminant Functions for Classification

TL;DR

This paper introduces a series of low-dimensional, interpretable kernels for classification that maintain high accuracy and fast training times, making them practical alternatives to traditional high-dimensional kernels.

Contribution

The authors develop a novel method for constructing low-dimensional, interpretable kernels that achieve competitive accuracy with reduced training complexity.

Findings

High accuracy comparable to traditional kernels

Significantly faster training times

Maintains interpretability of the model

Abstract

Kernels are often developed and used as implicit mapping functions that show impressive predictive power due to their high-dimensional feature space representations. In this study, we gradually construct a series of simple feature maps that lead to a collection of interpretable low-dimensional kernels. At each step, we keep the original features and make sure that the increase in the dimension of input data is extremely low, so that the resulting discriminant functions remain interpretable and amenable to fast training. Despite our persistence on interpretability, we obtain high accuracy results even without in-depth hyperparameter tuning. Comparison of our results against several well-known kernels on benchmark datasets show that the proposed kernels are competitive in terms of prediction accuracy, while the training times are significantly lower than those obtained with state-of-the-art kernel implementations.