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：

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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，