Generalization bound for kernel similarity learning

Abstract

Similarity learning has received a large amount of interest and is an important tool for many scientific and industrial applications. In this framework, we wish to infer the distance (similarity) between points with respect to an arbitrary distance function $d$. Here, we formulate the problem as a regression from a feature space $\mathcal{X}$ to an arbitrary vector space $\mathcal{Y}$, where the Euclidean distance is proportional to $d$. We then give Rademacher complexity bounds on the generalization error. We find that with high probability, the complexity is bounded by the maximum of the radius of $\mathcal{X}$ and the radius of $\mathcal{Y}$.