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

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.

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