The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
그래프 임베딩의 목적은 그래프 데이터에 대한 저차원 임베딩 함수를 학습하는 것입니다. 기존 방법은 일반적으로 최대 우도 추정(MLE)에 의존하고 조건부 평균 추정(CME)을 통해 임베딩 기능을 학습하는 경우가 많습니다. 그러나 MLE는 이상치 오염에 취약한 것으로 잘 알려져 있습니다. 또한 CME는 그래프 삽입 방법의 적용 가능성을 제한된 범위의 그래프 데이터로 제한할 수 있습니다. 이러한 문제를 해결하기 위해 본 논문에서는 그래프 임베딩을 위한 새로운 방법을 제안합니다. 견고한 비율 그래프 임베딩 (RRGE). RRGE는 주어진 데이터 벡터에 대한 링크 가중치의 조건부 확률 분포와 한계 확률 분포 간의 비율 추정을 기반으로 하며 CME 기반 방법보다 더 넓은 범위의 그래프 데이터에 적용할 수 있습니다. 또한, 이상값에 대한 강력한 추정을 달성하기 위해 표준 교차 엔트로피에 대한 강력한 대안인 γ-교차 엔트로피를 사용하여 비율을 추정합니다. 인공 데이터에 대한 수치 실험에 따르면 RRGE는 이상값에 대해 강력하며 CME 기반 방법이 전혀 작동하지 않는 경우에도 잘 수행됩니다. 마지막으로 제안된 방법의 성능은 신경망을 사용하여 실제 데이터 세트에서 시연됩니다.
Kaito SATTA
Future University Hakodate
Hiroaki SASAKI
Future University Hakodate
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Kaito SATTA, Hiroaki SASAKI, "Graph Embedding with Outlier-Robust Ratio Estimation" in IEICE TRANSACTIONS on Information,
vol. E105-D, no. 10, pp. 1812-1816, October 2022, doi: 10.1587/transinf.2022EDL8033.
Abstract: The purpose of graph embedding is to learn a lower-dimensional embedding function for graph data. Existing methods usually rely on maximum likelihood estimation (MLE), and often learn an embedding function through conditional mean estimation (CME). However, MLE is well-known to be vulnerable to the contamination of outliers. Furthermore, CME might restrict the applicability of the graph embedding methods to a limited range of graph data. To cope with these problems, this paper proposes a novel method for graph embedding called the robust ratio graph embedding (RRGE). RRGE is based on the ratio estimation between the conditional and marginal probability distributions of link weights given data vectors, and would be applicable to a wider-range of graph data than CME-based methods. Moreover, to achieve outlier-robust estimation, the ratio is estimated with the γ-cross entropy, which is a robust alternative to the standard cross entropy. Numerical experiments on artificial data show that RRGE is robust against outliers and performs well even when CME-based methods do not work at all. Finally, the performance of the proposed method is demonstrated on realworld datasets using neural networks.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2022EDL8033/_p
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@ARTICLE{e105-d_10_1812,
author={Kaito SATTA, Hiroaki SASAKI, },
journal={IEICE TRANSACTIONS on Information},
title={Graph Embedding with Outlier-Robust Ratio Estimation},
year={2022},
volume={E105-D},
number={10},
pages={1812-1816},
abstract={The purpose of graph embedding is to learn a lower-dimensional embedding function for graph data. Existing methods usually rely on maximum likelihood estimation (MLE), and often learn an embedding function through conditional mean estimation (CME). However, MLE is well-known to be vulnerable to the contamination of outliers. Furthermore, CME might restrict the applicability of the graph embedding methods to a limited range of graph data. To cope with these problems, this paper proposes a novel method for graph embedding called the robust ratio graph embedding (RRGE). RRGE is based on the ratio estimation between the conditional and marginal probability distributions of link weights given data vectors, and would be applicable to a wider-range of graph data than CME-based methods. Moreover, to achieve outlier-robust estimation, the ratio is estimated with the γ-cross entropy, which is a robust alternative to the standard cross entropy. Numerical experiments on artificial data show that RRGE is robust against outliers and performs well even when CME-based methods do not work at all. Finally, the performance of the proposed method is demonstrated on realworld datasets using neural networks.},
keywords={},
doi={10.1587/transinf.2022EDL8033},
ISSN={1745-1361},
month={October},}
부
TY - JOUR
TI - Graph Embedding with Outlier-Robust Ratio Estimation
T2 - IEICE TRANSACTIONS on Information
SP - 1812
EP - 1816
AU - Kaito SATTA
AU - Hiroaki SASAKI
PY - 2022
DO - 10.1587/transinf.2022EDL8033
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E105-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 2022
AB - The purpose of graph embedding is to learn a lower-dimensional embedding function for graph data. Existing methods usually rely on maximum likelihood estimation (MLE), and often learn an embedding function through conditional mean estimation (CME). However, MLE is well-known to be vulnerable to the contamination of outliers. Furthermore, CME might restrict the applicability of the graph embedding methods to a limited range of graph data. To cope with these problems, this paper proposes a novel method for graph embedding called the robust ratio graph embedding (RRGE). RRGE is based on the ratio estimation between the conditional and marginal probability distributions of link weights given data vectors, and would be applicable to a wider-range of graph data than CME-based methods. Moreover, to achieve outlier-robust estimation, the ratio is estimated with the γ-cross entropy, which is a robust alternative to the standard cross entropy. Numerical experiments on artificial data show that RRGE is robust against outliers and performs well even when CME-based methods do not work at all. Finally, the performance of the proposed method is demonstrated on realworld datasets using neural networks.
ER -