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".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
A previsão de link, o problema computacional de determinar se existe um link entre dois objetos, é importante no aprendizado de máquina e na mineração de dados. A previsão de links baseada em recursos, na qual os vetores de recursos dos dois objetos são fornecidos, é de particular interesse porque também pode ser usada para vários problemas relacionados à identificação. Embora a máquina de fatoração e a máquina de fatoração de ordem superior (HOFM) sejam amplamente utilizadas para previsão de links baseada em recursos, elas usam combinações de recursos não apenas entre os dois objetos, mas também do mesmo objeto. As combinações de recursos do mesmo objeto são irrelevantes para os principais problemas de previsão de links, como a previsão de identidade, porque usá-las aumenta o custo computacional e degrada a precisão. Neste artigo, apresentamos novos modelos que usam combinações de recursos de ordem superior apenas entre os dois objetos. Como não havia algoritmos para calcular com eficiência combinações de recursos de ordem superior apenas em dois objetos, derivamos um aproveitando os resultados relatados e obtidos recentemente do cálculo do kernel ANOVA. Apresentamos um algoritmo eficiente de descida de coordenadas para os modelos propostos. Também melhoramos a eficácia do existente para o HOFM. Além disso, estendemos os modelos propostos para uma rede neural profunda. Resultados experimentais demonstraram a eficácia dos nossos modelos propostos.
Kyohei ATARASHI
Hokkaido University
Satoshi OYAMA
Hokkaido University,RIKEN AIP
Masahito KURIHARA
Hokkaido University
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Kyohei ATARASHI, Satoshi OYAMA, Masahito KURIHARA, "Link Prediction Using Higher-Order Feature Combinations across Objects" in IEICE TRANSACTIONS on Information,
vol. E103-D, no. 8, pp. 1833-1842, August 2020, doi: 10.1587/transinf.2019EDP7266.
Abstract: Link prediction, the computational problem of determining whether there is a link between two objects, is important in machine learning and data mining. Feature-based link prediction, in which the feature vectors of the two objects are given, is of particular interest because it can also be used for various identification-related problems. Although the factorization machine and the higher-order factorization machine (HOFM) are widely used for feature-based link prediction, they use feature combinations not only across the two objects but also from the same object. Feature combinations from the same object are irrelevant to major link prediction problems such as predicting identity because using them increases computational cost and degrades accuracy. In this paper, we present novel models that use higher-order feature combinations only across the two objects. Since there were no algorithms for efficiently computing higher-order feature combinations only across two objects, we derive one by leveraging reported and newly obtained results of calculating the ANOVA kernel. We present an efficient coordinate descent algorithm for proposed models. We also improve the effectiveness of the existing one for the HOFM. Furthermore, we extend proposed models to a deep neural network. Experimental results demonstrated the effectiveness of our proposed models.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2019EDP7266/_p
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@ARTICLE{e103-d_8_1833,
author={Kyohei ATARASHI, Satoshi OYAMA, Masahito KURIHARA, },
journal={IEICE TRANSACTIONS on Information},
title={Link Prediction Using Higher-Order Feature Combinations across Objects},
year={2020},
volume={E103-D},
number={8},
pages={1833-1842},
abstract={Link prediction, the computational problem of determining whether there is a link between two objects, is important in machine learning and data mining. Feature-based link prediction, in which the feature vectors of the two objects are given, is of particular interest because it can also be used for various identification-related problems. Although the factorization machine and the higher-order factorization machine (HOFM) are widely used for feature-based link prediction, they use feature combinations not only across the two objects but also from the same object. Feature combinations from the same object are irrelevant to major link prediction problems such as predicting identity because using them increases computational cost and degrades accuracy. In this paper, we present novel models that use higher-order feature combinations only across the two objects. Since there were no algorithms for efficiently computing higher-order feature combinations only across two objects, we derive one by leveraging reported and newly obtained results of calculating the ANOVA kernel. We present an efficient coordinate descent algorithm for proposed models. We also improve the effectiveness of the existing one for the HOFM. Furthermore, we extend proposed models to a deep neural network. Experimental results demonstrated the effectiveness of our proposed models.},
keywords={},
doi={10.1587/transinf.2019EDP7266},
ISSN={1745-1361},
month={August},}
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TY - JOUR
TI - Link Prediction Using Higher-Order Feature Combinations across Objects
T2 - IEICE TRANSACTIONS on Information
SP - 1833
EP - 1842
AU - Kyohei ATARASHI
AU - Satoshi OYAMA
AU - Masahito KURIHARA
PY - 2020
DO - 10.1587/transinf.2019EDP7266
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E103-D
IS - 8
JA - IEICE TRANSACTIONS on Information
Y1 - August 2020
AB - Link prediction, the computational problem of determining whether there is a link between two objects, is important in machine learning and data mining. Feature-based link prediction, in which the feature vectors of the two objects are given, is of particular interest because it can also be used for various identification-related problems. Although the factorization machine and the higher-order factorization machine (HOFM) are widely used for feature-based link prediction, they use feature combinations not only across the two objects but also from the same object. Feature combinations from the same object are irrelevant to major link prediction problems such as predicting identity because using them increases computational cost and degrades accuracy. In this paper, we present novel models that use higher-order feature combinations only across the two objects. Since there were no algorithms for efficiently computing higher-order feature combinations only across two objects, we derive one by leveraging reported and newly obtained results of calculating the ANOVA kernel. We present an efficient coordinate descent algorithm for proposed models. We also improve the effectiveness of the existing one for the HOFM. Furthermore, we extend proposed models to a deep neural network. Experimental results demonstrated the effectiveness of our proposed models.
ER -