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  Semi-supervised Learning with Explicit Relationship Regularization

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.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-002B-9A62-6 Version Permalink: http://hdl.handle.net/11858/00-001M-0000-002B-9A63-4
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arXiv:1602.03808.pdf (Preprint), 557KB
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arXiv:1602.03808.pdf
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File downloaded from arXiv at 2016-10-13 09:55 Accepted version of paper published at CVPR 2015, http://dx.doi.org/10.1109/CVPR.2015.7298831
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 Creators:
Kim, Kwang In1, Author              
Tompkin, James1, Author              
Pfister, Hanspeter1, Author
Theobalt, Christian2, Author              
Affiliations:
1External Organizations, ou_persistent22              
2Computer Graphics, MPI for Informatics, Max Planck Society, ou_40047              

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Free keywords: Computer Science, Computer Vision and Pattern Recognition, cs.CV,Computer Science, Learning, cs.LG
 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.

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Language(s): eng - English
 Dates: 2016-02-112016
 Publication Status: Published online
 Pages: 10 p.
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: arXiv: 1602.03808
URI: http://arxiv.org/abs/1602.03808
BibTex Citekey: KimarXiv1602.03808
 Degree: -

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