The convergence rate of a regularized ranking algorithm

In this paper, we investigate the generalization performance of a regularized ranking algorithm in a reproducing kernel Hilbert space associated with least square ranking loss. An explicit expression for the solution via a sampling operator is derived and plays an important role in our analysis. Convergence analysis for learning a ranking function is provided, based on a novel capacity independent approach, which is stronger than for previous studies of the ranking problem.