A Binary Classification Framework for Two-Stage Multiple Kernel Learning

TL;DR

This paper introduces a new binary classification framework for multiple kernel learning that simplifies the process, ensures positive definiteness, and demonstrates competitive performance across diverse datasets.

Contribution

It reformulates multiple kernel learning as a binary classification problem with positive definiteness constraints, enabling simpler and more scalable algorithms.

Findings

The proposed method performs well compared to existing MKL approaches.

It is conceptually simpler and more accessible to practitioners.

Experiments on nine datasets validate its effectiveness.

Abstract

With the advent of kernel methods, automating the task of specifying a suitable kernel has become increasingly important. In this context, the Multiple Kernel Learning (MKL) problem of finding a combination of pre-specified base kernels that is suitable for the task at hand has received significant attention from researchers. In this paper we show that Multiple Kernel Learning can be framed as a standard binary classification problem with additional constraints that ensure the positive definiteness of the learned kernel. Framing MKL in this way has the distinct advantage that it makes it easy to leverage the extensive research in binary classification to develop better performing and more scalable MKL algorithms that are conceptually simpler, and, arguably, more accessible to practitioners. Experiments on nine data sets from different domains show that, despite its simplicity, the proposed technique compares favorably with current leading MKL approaches.