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- W3210615002 abstract "Active learning maximizes the performance of the current learning model by soliciting benefits from unlabeled data. In its early stage with insufficient labels, finding representations that maintain a consistent hypothesis with the entire unlabeled pool is an intelligent paradigm. From a matrix perspective, this paradigm can be transformed into a sparse representation of the input matrix by the ℓ2,1-norm constraint. However, the ℓ2,1-norm constraint is used in the loss function, which may lead to the mean accumulation problem, resulting in sub-optimal mean-centering and low robustness to outliers. In this paper, to solve the aforementioned problems, we generalize the ℓ2,1-norm into the ℓ2,p-norm constraint, where 0<p≤1. Specifically, when p=1, the ℓ2,1 constraint derives the optimal mean-centering, avoiding biased feature transformations, and when 0<p<1, ℓ2,p regulates the sparsity of the loss function and regularization terms in the matrix representation, yielding robust active representations. Experiment results on benchmark databases demonstrate that our method outperforms other state-of-the-art active learning methods, particularly for outlier perturbations." @default.
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- W3210615002 date "2022-01-01" @default.
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- W3210615002 title "Robust active representation via <mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML display=inline id=d1e7912 altimg=si11.svg><mml:msub><mml:mrow><mml:mi>ℓ</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn><mml:mo>,</mml:mo><mml:mi>p</mml:mi></mml:mrow></mml:msub></mml:math>-norm constraints" @default.
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- W3210615002 doi "https://doi.org/10.1016/j.knosys.2021.107639" @default.
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