  Recently, metric learning and similarity learning have attracted a large
amount of interest. Many models and optimisation algorithms have been proposed.
However, there is relatively little work on the generalization analysis of such
methods. In this paper, we derive novel generalization bounds of metric and
similarity learning. In particular, we first show that the generalization
analysis reduces to the estimation of the Rademacher average over
"sums-of-i.i.d." sample-blocks related to the specific matrix norm. Then, we
derive generalization bounds for metric/similarity learning with different
matrix-norm regularisers by estimating their specific Rademacher complexities.
Our analysis indicates that sparse metric/similarity learning with $L^1$-norm
regularisation could lead to significantly better bounds than those with
Frobenius-norm regularisation. Our novel generalization analysis develops and
refines the techniques of U-statistics and Rademacher complexity analysis.
