A Novel Discriminative Virtual Label Regression Method for Unsupervised Feature Selection
- 1 January 2022
- journal article
- research article
- Published by Institute of Electronics, Information and Communications Engineers (IEICE) in IEICE Transactions on Information and Systems
- Vol. E105.D (1), 175-179
- https://doi.org/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.Keywords
This publication has 13 references indexed in Scilit:
- Whale optimization approaches for wrapper feature selectionApplied Soft Computing, 2018
- A Survey on semi-supervised feature selection methodsPattern Recognition, 2017
- Subspace learning-based graph regularized feature selectionKnowledge-Based Systems, 2016
- Feature Selection Based on Structured Sparsity: A Comprehensive StudyIEEE Transactions on Neural Networks and Learning Systems, 2016
- Alternating direction method of multipliers for non-negative matrix factorization with the beta-divergencePublished by Institute of Electrical and Electronics Engineers (IEEE) ,2014
- Unsupervised feature selection for multi-cluster dataPublished by Association for Computing Machinery (ACM) ,2010
- Efficient and Robust Feature Extraction by Maximum Margin CriterionIEEE Transactions on Neural Networks, 2006
- Document clustering by concept factorizationPublished by Association for Computing Machinery (ACM) ,2004
- Gene expression correlates of clinical prostate cancer behaviorCancer Cell, 2002
- Automatic classification of single facial imagesIEEE Transactions on Pattern Analysis and Machine Intelligence, 1999