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A new multi-view learning machine with incomplete data
Pattern Analysis and Applications ( IF 3.7 ) Pub Date : 2020-02-11 , DOI: 10.1007/s10044-020-00863-y
Changming Zhu , Chao Chen , Rigui Zhou , Lai Wei , Xiafen Zhang

Multi-view learning with incomplete views (MVL-IV) is a reliable algorithm to process incomplete datasets which consist of instances with missing views or features. In MVL-IV, it exploits the connections among multiple views and suggests that different views are generated from a shared subspace such that it can recover the missing views or features well while MVL-IV neglects two facts. One is that different views should always be generated from different subspaces. The other is that the information of view-based classifiers is useful to the design of MVL-IV. Thus, on the base of MVL-IV, we consider these two facts and develop a new multi-view learning with incomplete data (NMVL-IV). Related experiments on clustering, regression, classification, bipartite ranking, and image retrieval have validated that the proposed NMVL-IV can recover the incomplete data much better.

中文翻译:

数据不完整的新型多视图学习机

具有不完整视图的多视图学习(MVL-IV)是一种可靠的算法,可以处理不完整的数据集,该数据集包含具有丢失的视图或特征的实例。在MVL-IV中,它利用了多个视图之间的联系,并建议从一个共享子空间中生成不同的视图,以便它可以很好地恢复丢失的视图或特征,而MVL-IV则忽略了两个事实。一种是应该始终从不同的子空间生成不同的视图。另一个是基于视图的分类器的信息对于MVL-IV的设计很有用。因此,在MVL-IV的基础上,我们考虑了这两个事实,并开发了一种具有不完整数据的新的多视图学习(NMVL-IV)。有关聚类,回归,分类,二分排名,
更新日期:2020-02-11
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