Auto-weighted Multi-view Feature Selection with Graph Optimization
This work addresses the problem of handling high-dimensional data in multi-view learning for researchers and practitioners, but it is incremental as it builds on existing graph-based methods.
The paper tackles unsupervised multi-view feature selection by proposing a graph-based model that learns a consensus similarity graph across views, adds rank constraints for accuracy, and uses an auto-weighted framework for adaptive view weighting, demonstrating superiority over state-of-the-art methods in experiments.
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.