论文标题
多标签学习的新兴趋势
The Emerging Trends of Multi-Label Learning
论文作者
论文摘要
人类每天都会产生大量数据，这导致人们对应对大数据带来的多标签学习的巨大挑战的新努力越来越多。例如，极端的多标签分类是一个积极且快速增长的研究领域，涉及大量类别或标签的分类任务。利用大量数据在有限的监督下建立多标签分类模型对于实际应用等。除此之外，还为如何收获深度学习的强大学习能力以更好地捕获多标签学习中的标签依赖性，这是深度学习以解决现实世界中的关键的关键，这是巨大的努力。但是，人们指出的是，缺乏系统性研究，这些研究明确地着重于分析大数据时代的多标签学习的新兴趋势和新挑战。必须呼吁进行全面的调查来履行这一任务，并描述未来的研究指示和新应用。
Exabytes of data are generated daily by humans, leading to the growing need 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 an 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 has 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 fulfill this mission and delineate future research directions and new applications.
