Jan 11, 2023 · A non-negative supervised low-rank discriminant embedding model (NSLRDE) is proposed to improve the robustness of the algorithm.

Jan 11, 2023 · In order to enhance the robustness of NMF, we propose a new method of non-negative supervised low-rank discriminant embedded, which uses low ...

In addition, the algorithm uses low-rank representation learning and non-negative decomposition to further enhance the robustness of the algorithm. Finally, ...

Jan 11, 2023 · In addition, the algorithm uses low-rank representation learning and non-negative decomposition to further enhance the robustness of the ...

Among many feature extraction technologies, non-negative matrix factorization (NMF) technology ignores the global representation of data and focuses on the ...

Robust non-negative supervised low-rank discriminant embedding (NSLRDE) for feature extraction. https://doi.org/10.1007/s13042-022-01752-y.

Robust non-negative supervised low-rank discriminant embedding (NSLRDE) for feature extraction · Low-rank preserving embedding regression for robust image ...

Robust non-negative supervised low-rank discriminant embedding (NSLRDE) for feature extraction. Article. Full-text available. Jan 2023. Minghua Wan ...

Yan, T. Zhan, et al., Robust non-negative supervised low-rank discriminant embedding (NSLRDE) for feature extraction, Int. J. Mach. Learn. Cybern. (2023) 1–14.

... Robust non-negative supervised low-rank discriminant embedding (NSLRDE) for feature extraction. Wan M., Yan C., Zhan T., Tan H., Yang G. Q1. Springer Nature.