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Activity Number: 487 - Neural Networks, Deep Learning, and RKHS
Type: Contributed
Date/Time: Wednesday, August 1, 2018 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Learning and Data Science
Abstract #328350 Presentation
Title: Reproducing Kernels for Pairwise Learning
Author(s): Xin Guo* and Ting Hu and Qiang Wu and Ding-Xuan Zhou
Companies: The Hong Kong Polytechnic University and Wuhan University and Middle Tennessee State University and City University of Hong Kong
Keywords: pairwise learning; reproducing kernels; learning theory

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 reproducing kernel Hilbert space defined by the obtained kernel lies in the orthogonal complement of constant functions, and is thus a perfect hypothesis space for learning the scoring functions. We proved that the kernel space complexity is invariant after this transformation. We also obtained some relations between the integral operators before and after the kernel transformation.

Authors who are presenting talks have a * after their name.

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