Low-rank learning for feature selection in multi-label classification
Author:
Funder
Kyonggi University
Publisher
Elsevier BV
Subject
Artificial Intelligence,Computer Vision and Pattern Recognition,Signal Processing,Software
Reference39 articles.
1. Generative correlation discovery network for multi-label learning;Wang,2019
2. Local discriminative based sparse subspace learning for feature selection;Shang;Pattern Recognit.,2019
3. Low-rank sparse subspace for spectral clustering;Zhu;IEEE Trans. Knowl. Data Eng.,2018
4. Nonnegative Laplacian embedding guided subspace learning for unsupervised feature selection;Zhang;Pattern Recognit.,2019
5. Simultaneous feature selection and discretization based on mutual information;Sharmin;Pattern Recognit.,2019
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