  In this paper, we focus on the unsupervised multi-view feature selection
which tries to handle high dimensional data in the field of multi-view
learning. Although some graph-based methods have achieved satisfactory
performance, they ignore the underlying data structure across different views.
Besides, their pre-defined laplacian graphs are sensitive to the noises in the
original data space, and fail to get the optimal neighbor assignment. To
address the above problems, we propose a novel unsupervised multi-view feature
selection model based on graph learning, and the contributions are threefold:
(1) during the feature selection procedure, the consensus similarity graph
shared by different views is learned. Therefore, the proposed model can reveal
the data relationship from the feature subset. (2) a reasonable rank constraint
is added to optimize the similarity matrix to obtain more accurate information;
(3) an auto-weighted framework is presented to assign view weights adaptively,
and an effective alternative iterative algorithm is proposed to optimize the
problem. Experiments on various datasets demonstrate the superiority of the
proposed method compared with the state-of-the-art methods.
