# Cross-view Joint Learning for Mixed-Missing Multi-view Unsupervised Feature Selection

arXiv: https://arxiv.org/abs/2511.12261 · HTML: https://mlsanity.com/paper/2511.12261/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 39 references that are in the map.
- First posted on arXiv 15 Nov 2025.

## Builds on (most cited first, 6 of 6)

- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [Handling Missing Data with Graph Representation Learning](https://mlsanity.com/paper/2010.16418/) (2020)
- [Semantic Invariant Multi-view Clustering with Fully Incomplete Information](https://mlsanity.com/paper/2305.12743/) (2023)
- [Joint Multi-view Unsupervised Feature Selection and Graph Learning](https://mlsanity.com/paper/2204.08247/) (2022)
- [Joint Embedding Learning and Low-Rank Approximation: A Framework for Incomplete Multi-view Learning](https://mlsanity.com/paper/1812.10012/) (2018)
- [Unified View Imputation and Feature Selection Learning for Incomplete Multi-view Data](https://mlsanity.com/paper/2401.10549/) (2024)

## Abstract (arXiv)

Incomplete multi-view unsupervised feature selection (IMUFS), which aims to identify representative features from unlabeled multi-view data containing missing values, has received growing attention in recent years. Despite their promising performance, existing methods face three key challenges: 1) by focusing solely on the view-missing problem, they are not well-suited to the more prevalent mixed-missing scenario in practice, where some samples lack entire views or only partial features within views; 2) insufficient utilization of consistency and diversity across views limits the effectiveness of feature selection; and 3) the lack of theoretical analysis makes it unclear how feature selection and data imputation interact during the joint learning process. Being aware of these, we propose CLIM-FS, a novel IMUFS method designed to address the mixed-missing problem. Specifically, we integrate the imputation of both missing views and variables into a feature selection model based on nonnegative orthogonal matrix factorization, enabling the joint learning of feature selection and adaptive data imputation. Furthermore, we fully leverage consensus cluster structure and cross-view local geometrical structure to enhance the synergistic learning process. We also provide a theoretical analysis to clarify the underlying collaborative mechanism of CLIM-FS. Experimental results on eight real-world multi-view datasets demonstrate that CLIM-FS outperforms state-of-the-art methods.