multi-label learning is a rapidly growing research area that aims to predict
multiple labels from a single input data point. In the era of big data, tasks
involving multi-label classification (MLC) or ranking present significant and
intricate challenges, capturing considerable attentio
Scalable Label Distribution Learning (SLDL) is proposed for multi-label classification, where different labels are described as distributions in a latent space with asymmetric correlation, independent of the number of labels, resulting in little computational consumption.