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multilabel-classification

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ml-lab-sau / Low-rank-label-subspace-transformation-for-multi-label-learning-with-missing-labels

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The proposed method captures local and global correlations using Low Rank label subspace transformation for Multi-label learning with Missing Labels (LRMML). The model considers an auxiliary label matrix which facilitates the missing label information recovery.

machine-learning machine-learning-algorithms multilabel-classification mlc missing-labels lrmml low-rank-label-space
  • Updated Jan 28, 2024
  • MATLAB

ml-lab-sau / Auxiliary-Label-Embedding-for-Multi-label-Learning-with-Missing-Labels

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In this paper, we propose an approach for multi-label classification when label details are incomplete by learning auxiliary label matrix from the observed labels, and generating an embedding from learnt label correlations preserving the correlation structure in model coefficients.

multi-label multilabel multilabel-classification multi-label-learning missing-labels auxiliary-labels label-correlation-embedding
  • Updated Jan 28, 2024
  • MATLAB

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