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Robust Multi-label Feature Selection with Missing Labels
This project forked from aiskyeye-tju/mlmlfs-pr2017
Robust Multi-label Feature Selection with Missing Labels
% ======================================================================== % Robust Multi-label Feature Selection with Missing Labels, Version 1.0 % Copyright(c) 2016 P. Zhu et al. % All Rights Reserved. % % ---------------------------------------------------------------------- % It includes: % 1 -- Matlab codes of our method and evaluation metrics. % 2 -- Five datasets include Artificial, Birds, Reference, Social and Yeast under different missing labels. % % ---------------------------------------------------------------------- % Permission to use, copy, or modify this software and its documentation % for educational and research purposes only and without fee is here % granted, provided that this copyright notice and the original authors' % names appear on all copies and supporting documentation. This program % shall not be used, rewritten, or adapted as the basis of a commercial % software or hardware product without first obtaining permission of the % authors. The authors make no representations about the suitability of % this software for any purpose. It is provided "as is" without express % or implied warranty. % ---------------------------------------------------------------------- % % Please refer to the following paper @article{zhu2018multi, title={Multi-label feature selection with missing labels}, author={Zhu, Pengfei and Xu, Qian and Hu, Qinghua and Zhang, Changqing and Zhao, Hong}, journal={Pattern Recognition}, volume={74}, pages={488--502}, year={2018}, publisher={Elsevier} } % % Contact: {zhupengfei,xuqian912}@tju.edu.cn % ----------------------------------------------------------------------
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