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- W3208989687 abstract "Multi-task learning has been observed by many researchers, which supposes that different tasks can share a low-rank common yet latent subspace. It means learning multiple tasks jointly is better than learning them independently. In this paper, we propose two novel multi-task learning formulations based on two regularization terms, which can learn the optimal shared latent subspace by minimizing the exactly k minimal singular values. The proposed regularization terms are the more tight approximations of rank minimization than trace norm. But it's an NP-hard problem to solve the exact rank minimization problem. Therefore, we design a novel re-weighted based iterative strategy to solve our models, which can tactically handle the exact rank minimization problem by setting a large penalizing parameter. Experimental results on benchmark datasets demonstrate that our methods can correctly recover the low-rank structure shared across tasks, and outperform related multi-task learning methods." @default.
- W3208989687 created "2021-11-08" @default.
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- W3208989687 date "2021-10-26" @default.
- W3208989687 modified "2023-09-26" @default.
- W3208989687 title "New Tight Relaxations of Rank Minimization for Multi-Task Learning" @default.
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- W3208989687 doi "https://doi.org/10.1145/3459637.3482154" @default.
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