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|a 10.1109/TPAMI.2020.3007637
|2 doi
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|a eng
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| 100 |
1 |
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|a Zhang, Rui
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Robust Multi-Task Learning With Flexible Manifold Constraint
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| 264 |
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|c 2021
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| 336 |
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|a Text
|b txt
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Revised 12.05.2021
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a Multi-Task Learning attempts to explore and mine the sufficient information within multiple related tasks for the better solutions. However, the performance of the existing multi-task approaches would largely degenerate when dealing with the polluted data, i.e., outliers. In this paper, we propose a novel robust multi-task model by incorporating a flexible manifold constraint (FMC-MTL) and a robust loss. Specifically speaking, multi-task subspace is embedded with a relaxed and generalized Stiefel Manifold for considering point-wise correlation and preserving the data structure simultaneously. In addition, a robust loss function is developed to ensure the robustness to outliers by smoothly interpolating between l2,1-norm and squared Frobenius norm. Equipped with an efficient algorithm, FMC-MTL serves as a robust solution to tackling the severely polluted data. Moreover, extensive experiments are conducted to verify the superiority of our model. Compared to the state-of-the-art multi-task models, the proposed FMC-MTL model demonstrates remarkable robustness to the contaminated data
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| 650 |
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|a Journal Article
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| 700 |
1 |
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|a Zhang, Hongyuan
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Li, Xuelong
|e verfasserin
|4 aut
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| 773 |
0 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 43(2021), 6 vom: 30. Juni, Seite 2150-2157
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnns
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| 773 |
1 |
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|g volume:43
|g year:2021
|g number:6
|g day:30
|g month:06
|g pages:2150-2157
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|u http://dx.doi.org/10.1109/TPAMI.2020.3007637
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