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| 无监督多视图特征选择研究进展 |
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吴建生1,2,3, 李艳兰4, 黄冲5, 闵卫东1,2,3
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1.南昌大学 数学与计算机学院, 江西 南昌 330031;2.南昌大学 元宇宙研究院, 江西 南昌 330031;3.江西省智慧城市重点实验室 (南昌大学), 江西 南昌 330031;4.南昌大学 软件学院, 江西 南昌 330047;5.南昌大学 信息化办公室, 江西 南昌 330031
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| 摘要: |
| 多视图数据从不同角度描述数据对象, 数据在不同视图中的特征表示之间存在着相关性、互补性及多样性信息. 综合利用这些信息对多视图数据处理至关重要. 然而, 多视图数据通常具有高维度特点, 且常含有噪声特征, 这为多视图数据的处理与分析带来了许多困难. 无监督多视图特征选择无需样本标记信息, 从多个视图源的原始高维特征中学习更紧凑、更准确的特征表示以提高数据分析的效果, 在多视图数据处理领域起着重要作用. 根据已有的无监督多视图特征选择模型的工作机制的异同, 对这些模型进行归纳和总结, 分析其中存在的不足, 并指出未来研究的方向. |
| 关键词: 多视图数据 无监督特征选择 聚类 结构保持 流数据 |
| DOI：10.13328/j.cnki.jos.007168 |
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| 基金项目:国家自然科学基金(62066027, 62076117); 江西省自然科学基金(20212BAB212011); 江西省智慧城市重点实验室科技创新平台项目(20192BCD40002) |
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| Recent Advances in Unsupervised Multi-view Feature Selection |
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WU Jian-Sheng1,2,3, LI Yan-Lan4, HUANG Chong5, MIN Wei-Dong1,2,3
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1.School of Mathematics and Computer Sciences, Nanchang University, Nanchang 330031, China;2.Institute of Metaverse, Nanchang University, Nanchang 330031, China;3.Jiangxi Key Laboratory of Smart City (Nanchang University), Nanchang 330031, China;4.School of Software, Nanchang University, Nanchang 330047, China;5.Informatization Office, Nanchang University, Nanchang 330031, China
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| Abstract: |
| Multi-view data depicts objects from different perspectives, with features in different views exhibiting correlations, complementary, and diverse information. Therefore, it is crucial to make full use of this information for the processing of multi-view data. However, the processing and analysis of multi-view data will be difficult due to the inherent challenges of dealing with a vast number of features and the presence of noise features in multi-view data. Unsupervised multi-view feature selection, emerging as a critical component in multi-view data learning, efficiently learns more accurate and compact representations from the original high-dimensional multi-view data without relying on label information to remarkably improve the performance of data analysis. This study reviews and categorizes these models based on the similarities and differences in the working mechanisms of existing unsupervised multi-view feature selection models, while also detailing their limitations. Furthermore, this study points out promising future research directions in the field of unsupervised feature selection. |
| Key words: multi-view data unsupervised feature selection clustering structure preservation stream data |
