# Auto-weighted Multi-view Feature Selection with Graph Optimization

arXiv: https://arxiv.org/abs/2104.04906 · HTML: https://mlsanity.com/paper/2104.04906/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 47 references that are in the map.
- First posted on arXiv 11 Apr 2021.

## Builds on (most cited first, 2 of 2)

- [Simultaneously Learning Neighborship and Projection Matrix for Supervised Dimensionality Reduction](https://mlsanity.com/paper/1709.02896/) (2017)
- [Multiview Hessian Regularization for Image Annotation](https://mlsanity.com/paper/1904.10100/) (2019)

## Built on by (most cited first, 2 of 2)

- [K-means Derived Unsupervised Feature Selection using Improved ADMM](https://mlsanity.com/paper/2411.15197/) (2024)
- [Kernel Alignment-based Multi-view Unsupervised Feature Selection with Sample-level Adaptive Graph Learning](https://mlsanity.com/paper/2601.07288/) (2026)

## Abstract (arXiv)

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.