机器学习中原型学习研究进展

随着信息技术在社会各领域的深入渗透,人类社会所拥有的数据总量达到了一个前所未有的高度.一方面,海量数据为基于数据驱动的机器学习方法获取有价值的信息提供了充分的空间;另一方面,高维度、过冗余以及高噪声也是上述繁多、复杂数据的固有特性.为消除数据冗余、发现数据结构、提高数据质量,原型学习是一种行之有效的方式.通过寻找一个原型集来表示目标集,以从样本空间进行数据约简,在增强数据可用性的同时,提升机器学习算法的执行效率.其可行性在众多应用领域中已得到证明.因此,原型学习相关理论与方法的研究是当前机器学习领域的一个研究热点与重点.主要介绍了原型学习的研究背景和应用价值,概括介绍了各类原型学习相关方法的基本特性、原型的质量评估以及典型应用;接着,从原型学习的监督方式及模型设计两个视角重点介绍了原型学习的研究进展,其中,前者主要涉及无监督、半监督和全监督方式,后者包括基于相似度、行列式点过程、数据重构和低秩逼近这四大类原型学习方法;最后,对原型学习的未来发展方向进行了展望.

Prototype Learning in Machine Learning: A Literature Review

With the in-depth penetration of information technology in various fields, there are many data in the real world. This can help data-driven algorithms in machine learning obtain valuable knowledge. Meanwhile, high-dimension, excessive redundancy, and strong noise are inherent characteristics of these various and complex data. In order to eliminate redundancy, discover data structure, and improve data quality, prototype learning is developed. By finding a prototype set from the target set, the data in the sample space can be reduced, and then the efficiency and effectiveness of machine learning algorithms can be improved. Its feasibility has been proven in many applications. Thus, the research on prototype learning has been one of the hot and key research topics in the field of machine learning recently. This study mainly introduces the research background and application value of prototype learning. Meanwhile, it also provides an overview of specialties of various related methods in prototype learning, quality evaluation of prototypes, and typical applications. Then, the research progress of prototype learning with respect to supervision mode and model design is presented. In particular, the former involves unsupervision, semi-supervision, and full supervision mode, and the latter compares four kinds of prototype learning methods based on similarity, determinantal point process, data reconstruction, and low-rank approximation, respectively. Finally, this study looks forward to the future development of prototype learning.