标记分布学习

In a particular application, the importance of the different meanings of an ambiguous object is often different. The current framework of multi-label learning cannot match this phenomenon well. To deal with such situation, label distribution learning is proposed as a new learning framework, where an instance is not labeled by a label set, but by a label distribution. For each label in a label distribution, there is a real number called description degree, which represents the importance of the corresponding label. Compared with multi-label learning, label distribution learning is more general, flexible, and expressive. It is a fresh try to solve the ambiguity problem in learning. Moreover, both single-label and multi-label learning can be viewed as special cases of label distribution learning. Thus the research on label distribution learning might help to solve the problems in single-label and multi-label learning. This project is one of the first explorations of label distribution learning, which will study its theory, algorithms, applications, and uses in learning from crowds and the utilization of label correlation. This project endeavors to propose a basic theoretical framework and several algorithms for label distribution learning, and prove their values in real applications. It will result in 8-10 high-quality papers, 2-3 patents and training of 6-8 graduate students.

对一个特定的应用来说，一个多义性对象不同含义的重要性往往是不同的，而现有的多标记学习框架不能很好的匹配这一现象。标记分布学习是为此提出的一种新型学习框架。在该框架内，一个示例不是由一个标记集，而是由一个标记分布来标注。标记分布中对应每个标记都有一个表示其重要性的实数值，称为描述度。标记分布学习相较多标记学习展现出更多的一般性、灵活性和表达力，是解决多义性问题的一种崭新尝试。并且由于单标记和多标记学习都可以看作标记分布学习的特例，对标记分布学习的研究也有可能对解决单标记和多标记学习中的问题起到启发和促进作用。本项目是对标记分布学习最初的探索之一，将研究其基础理论、算法、应用，以及其在群学习和利用标记间相关性方面的作用。本项目预期将建立一个标记分布学习基本理论框架，提出若干标记分布学习算法，并通过实际应用证明其价值。预期成果包括8-10篇高质量论文、2-3项发明专利，以及培养6-8名研究生。

本项目建立了一个新型机器学习理论框架：标记分布学习，在该框架内提出了若干个标记分布学习算法，并应用于人脸年龄估计、头部姿态估计、人群计数等多个实际问题。项目成果已发表或录用论文21篇，其中SCI源5篇，EI源21篇，CCF A类论文11篇。申请国家发明专利12项，其中2项已授权。培养博士后2名，研究生22名，其中包括博士研究生4名，硕士研究生18名。各项指标均已大幅超出申请书中提出的预期成果。