Unsupervised Feature Selection Based on Reconstruction Error Minimization

Sheng Yang, Rui Zhang, Feiping Nie, Xuelong Li

科研成果: 书/报告/会议事项章节会议稿件同行评审

12 引用 (Scopus)

摘要

In this paper, we propose a novel unsupervised feature selection method, which is to minimize the data reconstruction error between each sample and a linear combination of its neighbors. Different from the conventional reconstruction-based feature selection method, we impose a nonnegative orthogonal constraint on the reconstruction weight matrix, so that an ideal neighbor assignment is adaptively captured. To enhance the robustness of the residual term and select the most valuable features, {\ell -{2,1}}-norm is applied to both reconstruction error term and feature selection matrix. At last, we derive an iterative algorithm to effectively solve the proposed objective function, and perform extensive experiments on four benchmark datasets to validate the effectiveness of the proposed method.

源语言英语
主期刊名2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings
出版商Institute of Electrical and Electronics Engineers Inc.
2107-2111
页数5
ISBN(电子版)9781479981311
DOI
出版状态已出版 - 5月 2019
活动44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Brighton, 英国
期限: 12 5月 201917 5月 2019

出版系列

姓名ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
2019-May
ISSN(印刷版)1520-6149

会议

会议44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019
国家/地区英国
Brighton
时期12/05/1917/05/19

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