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  • RelationshipGuidedRegularization

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Semi-supervised learning with explicit relationship regularization

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Published
  • Kwang In Kim
  • James Tompkin
  • Hanspeter Pfister
  • Christian Theobalt
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Publication date8/06/2015
Host publicationComputer Vision and Pattern Recognition (CVPR), 2015 IEEE Conference on
PublisherIEEE
Pages2188-2196
Number of pages9
Original languageEnglish

Abstract

In many learning tasks, the structure of the target space of a function holds rich information about the relationships between evaluations of functions on different data points. Existing approaches attempt to exploit this relationship information implicitly by enforcing smoothness on function evaluations only. However, what happens if we explicitly regularize the relationships between function evaluations? Inspired by homophily, we regularize based on a smooth relationship function, either defined from the data or with labels. In experiments, we demonstrate that this significantly improves the performance of state-of-the-art algorithms in semi-supervised classification and in spectral data embedding for constrained clustering and dimensionality reduction.

Bibliographic note

©2015 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.