Multi-label learning with missing features and labels and its application to text categorization-Reference-Cited by-同舟云学术

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Multi-label learning with missing features and labels and its application to text categorization

Published:2022-05 Issue: Volume:14 Page:200086
ISSN:2667-3053
Container-title:Intelligent Systems with Applications
language:en
Short-container-title:Intelligent Systems with Applications

Author:

Hao Xiuyan,Huang Jun,Qin Feng,Zheng Xiao

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,Computer Vision and Pattern Recognition,Signal Processing,Computer Science (miscellaneous)

Reference45 articles.

1. Babbar, R., & Scholkopf, B. (2019). Data scarcity, robustness and extreme multi-label classification,. (pp. 1329–1351).

2. A fast iterative shrinkage-thresholding algorithm for linear inverse problems;Beck;SIAM journal on imaging sciences,2009

3. Sparse local embeddings for extreme multi-label classification;Bhatia,2015

4. Learning multi-label scene classification;Boutell;Pattern Recognition,2004

5. A singular value thresholding algorithm for matrix completion;Cai;SIAM Journal on Optimization,2010

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