IEEE Trans Pattern Anal Mach Intell. 2022 Nov;44(11):7955-7974. doi: 10.1109/TPAMI.2021.3119334. Epub 2022 Oct 4.
Exabytes of data are generated daily by humans, leading to the growing needs for new efforts in dealing with the grand challenges for multi-label learning brought by big data. For example, extreme multi-label classification is an active and rapidly growing research area that deals with classification tasks with extremely large number of classes or labels; utilizing massive data with limited supervision to build a multi-label classification model becomes valuable for practical applications, etc. Besides these, there are tremendous efforts on how to harvest the strong learning capability of deep learning to better capture the label dependencies in multi-label learning, which is the key for deep learning to address real-world classification tasks. However, it is noted that there have been a lack of systemic studies that focus explicitly on analyzing the emerging trends and new challenges of multi-label learning in the era of big data. It is imperative to call for a comprehensive survey to fulfil this mission and delineate future research directions and new applications.
人类每天产生的大量数据，使得人们需要不断努力，以应对大数据给多标签学习带来的巨大挑战。例如，极端多标签分类是一个活跃且快速发展的研究领域，涉及到具有极多类别或标签的分类任务；利用有限的监督数据构建多标签分类模型对于实际应用变得非常有价值，等等。除此之外，人们还在努力探索如何利用深度学习的强大学习能力来更好地捕捉多标签学习中的标签依赖性，这是深度学习解决实际分类任务的关键。然而，值得注意的是，目前缺乏系统的研究来明确分析大数据时代多标签学习的新兴趋势和新挑战。因此，有必要进行全面的调查来完成这一任务，并描绘未来的研究方向和新的应用。
