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  • RelationshipGuidedRegularization

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Semi-supervised learning with explicit relationship regularization

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paper

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Semi-supervised learning with explicit relationship regularization. / Kim, Kwang In; Tompkin, James; Pfister, Hanspeter ; Theobalt, Christian.

Computer Vision and Pattern Recognition (CVPR), 2015 IEEE Conference on . IEEE, 2015. p. 2188-2196.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paper

Harvard

Kim, KI, Tompkin, J, Pfister, H & Theobalt, C 2015, Semi-supervised learning with explicit relationship regularization. in Computer Vision and Pattern Recognition (CVPR), 2015 IEEE Conference on . IEEE, pp. 2188-2196. https://doi.org/10.1109/CVPR.2015.7298831

APA

Kim, K. I., Tompkin, J., Pfister, H., & Theobalt, C. (2015). Semi-supervised learning with explicit relationship regularization. In Computer Vision and Pattern Recognition (CVPR), 2015 IEEE Conference on (pp. 2188-2196). IEEE. https://doi.org/10.1109/CVPR.2015.7298831

Vancouver

Kim KI, Tompkin J, Pfister H, Theobalt C. Semi-supervised learning with explicit relationship regularization. In Computer Vision and Pattern Recognition (CVPR), 2015 IEEE Conference on . IEEE. 2015. p. 2188-2196 https://doi.org/10.1109/CVPR.2015.7298831

Author

Kim, Kwang In ; Tompkin, James ; Pfister, Hanspeter ; Theobalt, Christian. / Semi-supervised learning with explicit relationship regularization. Computer Vision and Pattern Recognition (CVPR), 2015 IEEE Conference on . IEEE, 2015. pp. 2188-2196

Bibtex

@inproceedings{96bc1c90faa147e0afaa9d56084f719d,
title = "Semi-supervised learning with explicit relationship regularization",
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.",
author = "Kim, {Kwang In} and James Tompkin and Hanspeter Pfister and Christian Theobalt",
note = "{\circledC}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.",
year = "2015",
month = "6",
day = "8",
doi = "10.1109/CVPR.2015.7298831",
language = "English",
pages = "2188--2196",
booktitle = "Computer Vision and Pattern Recognition (CVPR), 2015 IEEE Conference on",
publisher = "IEEE",

}

RIS

TY - GEN

T1 - Semi-supervised learning with explicit relationship regularization

AU - Kim, Kwang In

AU - Tompkin, James

AU - Pfister, Hanspeter

AU - Theobalt, Christian

N1 - ©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.

PY - 2015/6/8

Y1 - 2015/6/8

N2 - 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.

AB - 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.

U2 - 10.1109/CVPR.2015.7298831

DO - 10.1109/CVPR.2015.7298831

M3 - Conference contribution/Paper

SP - 2188

EP - 2196

BT - Computer Vision and Pattern Recognition (CVPR), 2015 IEEE Conference on

PB - IEEE

ER -