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Semi-supervised Learning with Explicit Relationship Regularization

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons45610

Theobalt,  Christian
Computer Graphics, MPI for Informatics, Max Planck Society;

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Fulltext (public)

arXiv:1602.03808.pdf
(Preprint), 557KB

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Citation

Kim, K. I., Tompkin, J., Pfister, H., & Theobalt, C. (2016). Semi-supervised Learning with Explicit Relationship Regularization. Retrieved from http://arxiv.org/abs/1602.03808.


Cite as: http://hdl.handle.net/11858/00-001M-0000-002B-9A62-6
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.