Relative Comparison Kernel Learning with Auxiliary Kernels

TL;DR

This paper introduces a convex optimization method for learning positive semidefinite kernels from limited human-provided relative comparisons, leveraging auxiliary kernels to improve generalization in data-scarce scenarios.

Contribution

It proposes a novel kernel learning approach combining auxiliary kernels with directly learned kernels, enhancing performance with few relative comparisons.

Findings

Outperforms methods without auxiliary kernels in low-data settings

Generalizes better to out-of-sample comparisons

Achieves comparable results to metric learning methods

Abstract

In this work we consider the problem of learning a positive semidefinite kernel matrix from relative comparisons of the form: "object A is more similar to object B than it is to C", where comparisons are given by humans. Existing solutions to this problem assume many comparisons are provided to learn a high quality kernel. However, this can be considered unrealistic for many real-world tasks since relative assessments require human input, which is often costly or difficult to obtain. Because of this, only a limited number of these comparisons may be provided. In this work, we explore methods for aiding the process of learning a kernel with the help of auxiliary kernels built from more easily extractable information regarding the relationships among objects. We propose a new kernel learning approach in which the target kernel is defined as a conic combination of auxiliary kernels and a kernel whose elements are learned directly. We formulate a convex optimization to solve for this target kernel that adds only minor overhead to methods that use no auxiliary information. Empirical results show that in the presence of few training relative comparisons, our method can learn kernels that generalize to more out-of-sample comparisons than methods that do not utilize auxiliary information, as well as similar methods that learn metrics over objects.