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
웹 관련 다중 레이블 분류 문제의 데이터 크기가 계속 증가함에 따라 레이블 공간도 엄청나게 커졌습니다. 예를 들어, 웹 페이지 태그 지정 및 전자 상거래 추천 작업에 나타나는 레이블 수는 수십만 또는 수백만에 이릅니다. 본 논문에서는 극단적인 다중 레이블 학습을 위한 새로운 접근 방식인 그래프 분할 트리(GPT)를 제안합니다. 트리의 내부 노드에서 GPT는 근사치를 고려하여 특징 공간을 분할하기 위한 선형 구분 기호를 학습합니다. k-레이블 벡터의 가장 가까운 이웃 그래프. 또한 선형 이진 분류기를 학습하기 위한 간단한 순차 최적화 절차도 개발했습니다. 대규모 실제 데이터 세트에 대한 광범위한 실험을 통해 우리의 방법이 최신 트리 기반 방법보다 빠른 예측을 유지하면서 더 나은 예측 정확도를 달성한다는 것을 보여주었습니다.
Yukihiro TAGAMI
Yahoo Japan Corporation,Kyoto University
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부
Yukihiro TAGAMI, "Recursive Nearest Neighbor Graph Partitioning for Extreme Multi-Label Learning" in IEICE TRANSACTIONS on Information,
vol. E102-D, no. 3, pp. 579-587, March 2019, doi: 10.1587/transinf.2018EDP7106.
Abstract: As the data size of Web-related multi-label classification problems continues to increase, the label space has also grown extremely large. For example, the number of labels appearing in Web page tagging and E-commerce recommendation tasks reaches hundreds of thousands or even millions. In this paper, we propose a graph partitioning tree (GPT), which is a novel approach for extreme multi-label learning. At an internal node of the tree, the GPT learns a linear separator to partition a feature space, considering approximate k-nearest neighbor graph of the label vectors. We also developed a simple sequential optimization procedure for learning the linear binary classifiers. Extensive experiments on large-scale real-world data sets showed that our method achieves better prediction accuracy than state-of-the-art tree-based methods, while maintaining fast prediction.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2018EDP7106/_p
부
@ARTICLE{e102-d_3_579,
author={Yukihiro TAGAMI, },
journal={IEICE TRANSACTIONS on Information},
title={Recursive Nearest Neighbor Graph Partitioning for Extreme Multi-Label Learning},
year={2019},
volume={E102-D},
number={3},
pages={579-587},
abstract={As the data size of Web-related multi-label classification problems continues to increase, the label space has also grown extremely large. For example, the number of labels appearing in Web page tagging and E-commerce recommendation tasks reaches hundreds of thousands or even millions. In this paper, we propose a graph partitioning tree (GPT), which is a novel approach for extreme multi-label learning. At an internal node of the tree, the GPT learns a linear separator to partition a feature space, considering approximate k-nearest neighbor graph of the label vectors. We also developed a simple sequential optimization procedure for learning the linear binary classifiers. Extensive experiments on large-scale real-world data sets showed that our method achieves better prediction accuracy than state-of-the-art tree-based methods, while maintaining fast prediction.},
keywords={},
doi={10.1587/transinf.2018EDP7106},
ISSN={1745-1361},
month={March},}
부
TY - JOUR
TI - Recursive Nearest Neighbor Graph Partitioning for Extreme Multi-Label Learning
T2 - IEICE TRANSACTIONS on Information
SP - 579
EP - 587
AU - Yukihiro TAGAMI
PY - 2019
DO - 10.1587/transinf.2018EDP7106
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E102-D
IS - 3
JA - IEICE TRANSACTIONS on Information
Y1 - March 2019
AB - As the data size of Web-related multi-label classification problems continues to increase, the label space has also grown extremely large. For example, the number of labels appearing in Web page tagging and E-commerce recommendation tasks reaches hundreds of thousands or even millions. In this paper, we propose a graph partitioning tree (GPT), which is a novel approach for extreme multi-label learning. At an internal node of the tree, the GPT learns a linear separator to partition a feature space, considering approximate k-nearest neighbor graph of the label vectors. We also developed a simple sequential optimization procedure for learning the linear binary classifiers. Extensive experiments on large-scale real-world data sets showed that our method achieves better prediction accuracy than state-of-the-art tree-based methods, while maintaining fast prediction.
ER -