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
비지도 특징 선택은 고차원 데이터에 대처하는 중요한 차원 축소 기술입니다. 사전 라벨 정보가 필요하지 않아 최근 많은 주목을 받고 있습니다. 그러나 샘플의 식별 정보를 충분히 활용하지 못하여 특징 선택 성능에 영향을 미칠 수 있습니다. 이 문제를 해결하기 위해 이 편지에서는 감독되지 않은 기능 선택을 위한 새로운 차별적 가상 레이블 회귀 방법(DVLR)을 제안합니다. DVLR에서는 보다 차별적인 특징을 선택할 수 있는 부분 공간 학습 기반 특징 선택을 안내하는 가상 레이블 회귀 함수를 개발합니다. 또한 선형 판별 분석(LDA) 용어를 사용하여 모델을 더욱 구별적으로 만듭니다. 모델을 더욱 강력하게 만들고 보다 대표적인 기능을 선택하기 위해 다음을 부과합니다. ℓ2,1- 회귀 및 기능 선택 용어에 대한 표준입니다. 마지막으로 여러 공개 데이터 세트에 대해 광범위한 실험이 수행되었으며 결과는 제안된 DVRR이 여러 최첨단 비지도 특징 선택 방법보다 더 나은 성능을 달성한다는 것을 보여줍니다.
Zihao SONG
Yantai University
Peng SONG
Yantai University
Chao SHENG
Yantai University
Wenming ZHENG
Southeast University
Wenjing ZHANG
Yantai University
Shaokai LI
Yantai University
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
부
Zihao SONG, Peng SONG, Chao SHENG, Wenming ZHENG, Wenjing ZHANG, Shaokai LI, "A Novel Discriminative Virtual Label Regression Method for Unsupervised Feature Selection" in IEICE TRANSACTIONS on Information,
vol. E105-D, no. 1, pp. 175-179, January 2022, doi: 10.1587/transinf.2021EDL8067.
Abstract: Unsupervised Feature selection is an important dimensionality reduction technique to cope with high-dimensional data. It does not require prior label information, and has recently attracted much attention. However, it cannot fully utilize the discriminative information of samples, which may affect the feature selection performance. To tackle this problem, in this letter, we propose a novel discriminative virtual label regression method (DVLR) for unsupervised feature selection. In DVLR, we develop a virtual label regression function to guide the subspace learning based feature selection, which can select more discriminative features. Moreover, a linear discriminant analysis (LDA) term is used to make the model be more discriminative. To further make the model be more robust and select more representative features, we impose the ℓ2,1-norm on the regression and feature selection terms. Finally, extensive experiments are carried out on several public datasets, and the results demonstrate that our proposed DVLR achieves better performance than several state-of-the-art unsupervised feature selection methods.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2021EDL8067/_p
부
@ARTICLE{e105-d_1_175,
author={Zihao SONG, Peng SONG, Chao SHENG, Wenming ZHENG, Wenjing ZHANG, Shaokai LI, },
journal={IEICE TRANSACTIONS on Information},
title={A Novel Discriminative Virtual Label Regression Method for Unsupervised Feature Selection},
year={2022},
volume={E105-D},
number={1},
pages={175-179},
abstract={Unsupervised Feature selection is an important dimensionality reduction technique to cope with high-dimensional data. It does not require prior label information, and has recently attracted much attention. However, it cannot fully utilize the discriminative information of samples, which may affect the feature selection performance. To tackle this problem, in this letter, we propose a novel discriminative virtual label regression method (DVLR) for unsupervised feature selection. In DVLR, we develop a virtual label regression function to guide the subspace learning based feature selection, which can select more discriminative features. Moreover, a linear discriminant analysis (LDA) term is used to make the model be more discriminative. To further make the model be more robust and select more representative features, we impose the ℓ2,1-norm on the regression and feature selection terms. Finally, extensive experiments are carried out on several public datasets, and the results demonstrate that our proposed DVLR achieves better performance than several state-of-the-art unsupervised feature selection methods.},
keywords={},
doi={10.1587/transinf.2021EDL8067},
ISSN={1745-1361},
month={January},}
부
TY - JOUR
TI - A Novel Discriminative Virtual Label Regression Method for Unsupervised Feature Selection
T2 - IEICE TRANSACTIONS on Information
SP - 175
EP - 179
AU - Zihao SONG
AU - Peng SONG
AU - Chao SHENG
AU - Wenming ZHENG
AU - Wenjing ZHANG
AU - Shaokai LI
PY - 2022
DO - 10.1587/transinf.2021EDL8067
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E105-D
IS - 1
JA - IEICE TRANSACTIONS on Information
Y1 - January 2022
AB - Unsupervised Feature selection is an important dimensionality reduction technique to cope with high-dimensional data. It does not require prior label information, and has recently attracted much attention. However, it cannot fully utilize the discriminative information of samples, which may affect the feature selection performance. To tackle this problem, in this letter, we propose a novel discriminative virtual label regression method (DVLR) for unsupervised feature selection. In DVLR, we develop a virtual label regression function to guide the subspace learning based feature selection, which can select more discriminative features. Moreover, a linear discriminant analysis (LDA) term is used to make the model be more discriminative. To further make the model be more robust and select more representative features, we impose the ℓ2,1-norm on the regression and feature selection terms. Finally, extensive experiments are carried out on several public datasets, and the results demonstrate that our proposed DVLR achieves better performance than several state-of-the-art unsupervised feature selection methods.
ER -