不完整的图形学习:一个全面的调查调查
Riting Xia1, Huibo Liu1, Anchen Li2
1College of Computer Science, Inner Mongolia University, Hohhot, 010021, China.
概括
本综述介绍了不完整的图形学习,解决了由于缺失属性的图形数据分析中的挑战. 它对方法进行了分类,并讨论了强大的图形学习的未来研究方向.
科学领域:
- 图表学习学习图表学习
- 数据科学是数据科学.
- 机器学习 机器学习
背景情况:
- 图形学习方法对于从图形数据中提取信息至关重要.
- 当处理缺失属性时,现有的方法缺乏稳定性,导致性能不足.
- 不完整的图形学习解决了从不完整的图形数据中学习的挑战.
研究的目的:
- 提供关于不完整图形学习的文献的全面审查.
- 将不完整的图表分类,并定义关键概念和技术.
- 根据不完整的类型对现有方法进行分类.
主要方法:
- 关于不完整图形学习的系统文献综述.
- 不完整图的分类和学习方法 (属性不完整,属性缺失,混合缺失).
- 数据集,处理模式,评估指标和应用领域的总结.
主要成果:
- 基于属性不完整性的不完整图形学习方法的分类.
- 确定现有方法之间的共同点和差异.
- 在现场使用的资源和方法的总结.
结论:
- 这是第一个专门针对不完全图形学习的综述.
- 强调了挑战,并提出了未来的研究方向.
- 旨在为图形学习和相关领域的研究人员提供有价值的见解.
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