Learning shared subspace for multi-label dimensionality reduction via dependence maximization

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Elucidating image-to-set prediction: An analysis of models, losses and datasets (2019, background): “on [58,16,59,54]. Traditionally, the problem has been tackled from many different perspectives, from decomposing the problem into independent binary predictions [33,58] or modeling label correlations [1,43,28], to exploiting priors such as label noise and sparsity [16,19,45,54,59,2]. More recently, ”
Improving Pairwise Ranking for Multi-label Image Classification (2017, methodology): “We propose a new pairwise ranking loss function and a per-class threshold estimation method in a uniﬁed framework, improving existing ranking-based approaches in a principled manner. label dependency [1, 25], label sparsity [10, 12, 27], and label noise [33, 39].”