# C2IMUFS: Complementary and Consensus Learning-based Incomplete Multi-view Unsupervised Feature Selection

arXiv: https://arxiv.org/abs/2208.09736 · HTML: https://mlsanity.com/paper/2208.09736/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 53 references that are in the map.
- First posted on arXiv 20 Aug 2022.

## Builds on (most cited first, 5 of 5)

- [Robust and Efficient Subspace Segmentation via Least Squares Regression](https://mlsanity.com/paper/1404.6736/) (2014)
- [Online Unsupervised Multi-view Feature Selection](https://mlsanity.com/paper/1609.08286/) (2016)
- [Adaptive Collaborative Similarity Learning for Unsupervised Multi-view Feature Selection](https://mlsanity.com/paper/1904.11228/) (2019)
- [Effective Discriminative Feature Selection with Non-trivial Solutions](https://mlsanity.com/paper/1504.05408/) (2015)
- [Adaptive Graph-based Generalized Regression Model for Unsupervised Feature Selection](https://mlsanity.com/paper/2012.13892/) (2020)

## Built on by (most cited first, 1 of 1)

- [Knockoff-Guided Feature Selection via A Single Pre-trained Reinforced Agent](https://mlsanity.com/paper/2403.04015/) (2024)

## Abstract (arXiv)

Multi-view unsupervised feature selection (MUFS) has been demonstrated as an effective technique to reduce the dimensionality of multi-view unlabeled data. The existing methods assume that all of views are complete. However, multi-view data are usually incomplete, i.e., a part of instances are presented on some views but not all views. Besides, learning the complete similarity graph, as an important promising technology in existing MUFS methods, cannot achieve due to the missing views. In this paper, we propose a complementary and consensus learning-based incomplete multi-view unsupervised feature selection method (C2IMUFS) to address the aforementioned issues. Concretely, C2IMUFS integrates feature selection into an extended weighted non-negative matrix factorization model equipped with adaptive learning of view-weights and a sparse ℓ2,p-norm, which can offer better adaptability and flexibility. By the sparse linear combinations of multiple similarity matrices derived from different views, a complementary learning-guided similarity matrix reconstruction model is presented to obtain the complete similarity graph in each view. Furthermore, C2IMUFS learns a consensus clustering indicator matrix across different views and embeds it into a spectral graph term to preserve the local geometric structure. Comprehensive experimental results on real-world datasets demonstrate the effectiveness of C2IMUFS compared with state-of-the-art methods.