Robust Label Prediction via Label Propagation and Geodesic k-Nearest Neighbor in Online Semi-Supervised Learning
Summary :
This paper proposes a computationally efficient offline semi-supervised algorithm that yields a more accurate prediction than the label propagation algorithm, which is commonly used in online graph-based semi-supervised learning (SSL). Our proposed method is an offline method that is intended to assist online graph-based SSL algorithms. The efficacy of the tool in creating new learning algorithms of this type is demonstrated in numerical experiments.
- Publication
- IEICE TRANSACTIONS on Information Vol.E102-D No.8 pp.1537-1545
- Publication Date
- 2019/08/01
- Publicized
- 2019/04/26
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2018EDP7424
- Type of Manuscript
- PAPER
- Category
- Artificial Intelligence, Data Mining
Authors
Yuichiro WADA
Nagoya University
Siqiang SU
The Hong Kong Polytechnic University
Wataru KUMAGAI
RIKEN AIP
Takafumi KANAMORI
RIKEN AIP,Tokyo Institute of Technology
Keyword
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Yuichiro WADA, Siqiang SU, Wataru KUMAGAI, Takafumi KANAMORI, "Robust Label Prediction via Label Propagation and Geodesic k-Nearest Neighbor in Online Semi-Supervised Learning" in IEICE TRANSACTIONS on Information,
vol. E102-D, no. 8, pp. 1537-1545, August 2019, doi: 10.1587/transinf.2018EDP7424.
Abstract: This paper proposes a computationally efficient offline semi-supervised algorithm that yields a more accurate prediction than the label propagation algorithm, which is commonly used in online graph-based semi-supervised learning (SSL). Our proposed method is an offline method that is intended to assist online graph-based SSL algorithms. The efficacy of the tool in creating new learning algorithms of this type is demonstrated in numerical experiments.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2018EDP7424/_p
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@ARTICLE{e102-d_8_1537,
author={Yuichiro WADA, Siqiang SU, Wataru KUMAGAI, Takafumi KANAMORI, },
journal={IEICE TRANSACTIONS on Information},
title={Robust Label Prediction via Label Propagation and Geodesic k-Nearest Neighbor in Online Semi-Supervised Learning},
year={2019},
volume={E102-D},
number={8},
pages={1537-1545},
abstract={This paper proposes a computationally efficient offline semi-supervised algorithm that yields a more accurate prediction than the label propagation algorithm, which is commonly used in online graph-based semi-supervised learning (SSL). Our proposed method is an offline method that is intended to assist online graph-based SSL algorithms. The efficacy of the tool in creating new learning algorithms of this type is demonstrated in numerical experiments.},
keywords={},
doi={10.1587/transinf.2018EDP7424},
ISSN={1745-1361},
month={August},}
Copy
TY - JOUR
TI - Robust Label Prediction via Label Propagation and Geodesic k-Nearest Neighbor in Online Semi-Supervised Learning
T2 - IEICE TRANSACTIONS on Information
SP - 1537
EP - 1545
AU - Yuichiro WADA
AU - Siqiang SU
AU - Wataru KUMAGAI
AU - Takafumi KANAMORI
PY - 2019
DO - 10.1587/transinf.2018EDP7424
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E102-D
IS - 8
JA - IEICE TRANSACTIONS on Information
Y1 - August 2019
AB - This paper proposes a computationally efficient offline semi-supervised algorithm that yields a more accurate prediction than the label propagation algorithm, which is commonly used in online graph-based semi-supervised learning (SSL). Our proposed method is an offline method that is intended to assist online graph-based SSL algorithms. The efficacy of the tool in creating new learning algorithms of this type is demonstrated in numerical experiments.
ER -
English
