# Random Manifold Sampling and Joint Sparse Regularization for Multi-label Feature Selection

arXiv: https://arxiv.org/abs/2204.06445 · HTML: https://mlsanity.com/paper/2204.06445/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 35 references that are in the map.
- First posted on arXiv 13 Apr 2022.

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

- [A note on the triangle inequality for the Jaccard distance](https://mlsanity.com/paper/1612.02696/) (2016)
- [Weighted Random Walk Sampling for Multi-Relational Recommendation](https://mlsanity.com/paper/1703.00034/) (2017)

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

- [Multi-label feature selection based on binary hashing learning and dynamic graph constraints](https://mlsanity.com/paper/2503.13874/) (2025)

## Abstract (arXiv)

Multi-label learning is usually used to mine the correlation between features and labels, and feature selection can retain as much information as possible through a small number of features. ℓ2,1 regularization method can get sparse coefficient matrix, but it can not solve multicollinearity problem effectively. The model proposed in this paper can obtain the most relevant few features by solving the joint constrained optimization problems of ℓ2,1 and ℓF regularization.In manifold regularization, we implement random walk strategy based on joint information matrix, and get a highly robust neighborhood graph.In addition, we given the algorithm for solving the model and proved its convergence.Comparative experiments on real-world data sets show that the proposed method outperforms other methods.