JiaqiBao
JiaqiBao
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2024
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Robust embedding regression for semi-supervised learning
To utilize both labeled data and unlabeled data in real-world applications, semi-supervised learning is widely used as an effective …
Bao Jiaqi
,
Mineichi Kudo
,
Keigo Kimura
,
Lu Sun
PDF
DOI
Retargeted Regression Methods for Multi-label Learning
In Multi-Label Classification, utilizing label relationship is a key to improve classification accuracy. Label Space Dimension …
Keigo Kimura
,
Bao Jiaqi
,
Mineichi Kudo
,
Lu Sun
PDF
DOI
Relaxed local preserving regression for image feature extraction
Bao Jiaqi
,
Zhihui Lai
,
Xuechen Li
PDF
DOI
Redirected Transfer Learning for Robust Multi-Layer Subspace Learning
Unsupervised transfer learning methods usually exploit the labeled source data to learn a classifier for unlabeled target data with a …
Bao Jiaqi
,
Mineichi Kudo
,
Keigo Kimura
,
Lu Sun
PDF
Discriminative low-rank projection for robust subspace learning
The robustness to outliers, noises, and corruptions has been paid more attention recently to increase the performance in linear feature …
Zhihui Lai
,
Bao Jiaqi
,
Heng Kong
,
Minghua Wan
,
Guowei Yang
PDF
DOI
Robust Embedding Regression for Face Recognition
Classical subspace learning methods such as spectral regression (SR) and its sparse extensions are all two-step ways, which will lead …
Bao Jiaqi
,
Jianglin Lu
,
Zhihui Lai
,
Ning Liu
,
Yuwu Lu
PDF
DOI
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