Title: Reproducing kernels for pairwise learning
Authors: Xin Guo - The Hong Kong Polytechnic University (Hong Kong) [presenting]
Ting Hu - Wuhan University, The Hong Kong Polytechnic University (China)
Qiang Wu - Middle Tennessee State University (United States)
Abstract: Pairwise learning is a large family of learning algorithms for the problems where supervised labels are not available, but one has only the access to the differences between labels of each pair of sample points. For example, ranking, AUC maximization, metric learning, gradient learning, and so on. We studied a transform of reproducing kernels so that the obtained kernels fit the purpose of pairwise learning way better. The relation between the integral operators and the hypothesis spaces of the original and the transformed kernels are obtained.