Multi-view feature selection aims at obtaining a subset of informative features from heterogeneous feature domains. Recent graph based approaches mostly learn view-specific feature selection matrices by virtue of prepared single-view graphs, and weight the view-wise objectives to discriminate them. However, the majority of them encounter that (i) the dimensions of vectors for evaluating features in different views are inconsistent and (ii) the similarities attained by using features in the original high-dimensional space are inferior. As a result, the joint evaluation of heterogeneous features using view-specific selection matrices are inaccurate and immensely depend on ill-defined similarity relations. To overcome them, we propose the Multilevel projections with Adaptive neighbor graph model for unsupervised Multi-view Feature Selection (MAMFS). Our formulation learns the collaborative graph adapting to the adaptive $k$-nearest neighbors in subspaces, wherein the multilevel projections are involved including the weighted view-specific projections that discriminate different views and the joint projection that collaborates different views. Benefited from this, both of view-within information and the view-across information are jointly explored and made use of. Extensive simulations are conducted to validate the superiority of the proposed method compared to the state-of-the-art competitors.

多视图特征选择旨在从异构特征域中获得信息特征的子集。最近的基于图的方法大多借助准备好的单视图图来学习特定于视图的特征选择矩阵，并对加权的目标进行加权以区分它们。但是，它们中的大多数会遇到（i）用于评估不同视图中的特征的向量的尺寸不一致，并且（ii）通过使用原始高维空间中的特征获得的相似性较差。结果，使用特定于视图的选择矩阵对异构特征进行联合评估是不准确的，并且极大地依赖于定义不明确的相似关系。为了克服它们，我们提出了带有自适应邻居图模型的多级投影用于无监督的多视图特征选择（MAMFS）。$ķ$子空间中最接近的邻居，其中涉及多级投影，包括区分不同视图的加权特定于视图的投影和协作不同视图的联合投影。受益于此，共同探索和利用了视图内信息和视图间信息。与最先进的竞争对手相比，进行了广泛的仿真，以验证所提出方法的优越性。