# Feature Selection

**Repository Path**: jshncu/feature-selection

## Basic Information

- **Project Name**: Feature Selection
- **Description**: 本仓库内的代码实现了相关的特征选择方法
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2023-11-04
- **Last Updated**: 2025-06-24

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Feature Selection

#### 介绍
The codes shared in this repository implement the algorithms for the models proposed in the following papers. Welcome to use our models as the comparison methods in your papers. If you have any questions, you are welcome to send an email to me (jianshengwu@ncu.edu.cn). It is worth noting that some works require the SPAMS package (J. Mairal, F. Bach, J. Ponce, G. Sapiro, Online learning for matrix factorization and sparse coding, Journal of Machine Learning Research 11 (2) (2010) 19–60.).

 **_DLDSR (Dictionary Learning for Unsupervised Feature Selection via Dual Sparse Regression: https://doi.org/10.1007/s10489-021-02524-x )_** 

@article{FeatureSelection_DLDSR_Wu_2023, title={Dictionary Learning for Unsupervised Feature Selection via Dual Sparse Regression}, author={Wu, Jian-Sheng and Liu, Jing-Xin and Wu, Jun-Yun and Huang, Wei}, journal={Applied Intelligence}, volume={53}, pages={18840--18856}, year={2023}, publisher={Springer}, doi={10.1007/s10489-021-02524-x}, }

 **_JAMEL (Joint adaptive manifold and embedding learning for unsupervised feature selection: https://doi.org/10.1016/j.patcog.2020.107742 )_** 

 @article{FeatureSelection_JAMEL_Wu_2021, title={Joint adaptive manifold and embedding learning for unsupervised feature selection}, author={Wu, Jian-Sheng and Song, Meng-Xiao and Min, Weidong and Lai, Jian-Huang and Zheng, Wei-Shi}, journal={Pattern Recognition}, volume={112}, pages={107742}, year={2021}, doi={10.1016/j.patcog.2020.107742}, }

**_JCDLGL (Joint Cauchy Dictionary Learning and Graph Learning for Unsupervised Feature Selection: https://doi.org/10.1016/j.engappai.2024.108936 )_**

@article{FeatureSelection_JCDLGL_Liu_2024, title={Joint Cauchy Dictionary Learning and Graph Learning for Unsupervised Feature Selection}, author={Liu, Jing-Xin and Zeng, Qing-Peng, Wu, Jian-Sheng and Huang, Wei}, journal={Engineering Applications of Artificial Intelligence}, volume={136}, pages={108936}, year={2024}, doi={doi.org/10.1016/j.engappai.2024.108936}, } 


 **_MLCL (Multi-level correlation learning for multi-view unsupervised feature selection: https://doi.org/10.1016/j.knosys.2023.111073 )_** 
 
@article{MVFeatureSelection_MLCL_Wu_2023, title={Multi-level correlation learning for multi-view unsupervised feature selection}, author={Wu, Jian-Sheng and Gong, Jun-Xiao and Liu, Jing-Xin and Min, Weidong}, journal={Knowledge-Based Systems}, volume={281}, pages={111073}, year={2023}, doi={10.1016/j.knosys.2023.111073}, }

 **_CDSL (Collaborative and Discriminative Subspace Learning for unsupervised multi-view feature selection: https://doi.org/10.1016/j.engappai.2024.108145 )_**

 @article{MVFeatureSelection_CDSL_Wu_2024, title={Collaborative and Discriminative Subspace Learning for unsupervised multi-view feature selection}, author={Wu, Jian-Sheng and Li, Yanlan and Gong, Jun-Xiao and Min, Weidong}, journal={Engineering Applications of Artificial Intelligence}, volume={133}, pages={108145}, year={2024}, doi={10.1016/j.engappai.2024.108145}, }

 **_LIPS (Learning Missing Instances in Intact and Projection Spaces for Incomplete Multi-view Unsupervised Feature Selection: https://doi.org/10.1007/s10489-025-06406-4 )_**

 @article{IMVFeatureSelection_LIPS_Wu_2025, title={Learning Missing Instances in Intact and Projection Spaces for Incomplete Multi-view Unsupervised Feature Selection}, author={Wu, Jian-Sheng and Yu, Hong-Wei and Li, Yanlan and Min, Weidong}, journal={Applied Intelligence}, volume={55}, pages={510}, year={2025}, doi={10.1007/s10489-025-06406-4}, }

 **_CLSG (Confident Local Similarity Graphs for Unsupervised Feature Selection on Incomplete Multi-view Data: https://doi.org/10.1016/j.knosys.2025.113369 )_**

 @article{IMVFeatureSelection_CLSG_Yu_2025, title={Confident Local Similarity Graphs for Unsupervised Feature Selection on Incomplete Multi-view Data}, author={Yu, Hong-Wei and Wu, Jun-Yun and Wu, Jian-Sheng and Min, Weidong}, journal={Knowledge-Based Systems}, volume={316}, pages={113369}, year={2025}, doi={10.1016/j.knosys.2025.113369}, }

 **_DCVSG (High-order Aligned Deep Complementary and View-specific Similarity Graphs for Unsupervised Multi-view Feature Selection: https://doi.org/++++/+++++ )_**

 @article{MVFeatureSelection_DCVSG_Wu_2025, title={Confident Local Similarity Graphs for Unsupervised Feature Selection on Incomplete Multi-view Data}, author={Wu, Jian-Sheng and Yu, Jia-tao and Wu, Jun-Yun and Min, Weidong and Zheng Wei-Shi}, journal={Pattern Recognition}, volume={++++}, pages={++++}, year={++++}, doi={++++/++++}, }

#### 软件架构
软件架构说明


#### 安装教程

1.  xxxx
2.  xxxx
3.  xxxx

#### 使用说明

1.  xxxx
2.  xxxx
3.  xxxx

#### 参与贡献

1.  Fork 本仓库
2.  新建 Feat_xxx 分支
3.  提交代码
4.  新建 Pull Request


#### 特技

1.  使用 Readme\_XXX.md 来支持不同的语言，例如 Readme\_en.md, Readme\_zh.md
2.  Gitee 官方博客 [blog.gitee.com](https://blog.gitee.com)
3.  你可以 [https://gitee.com/explore](https://gitee.com/explore) 这个地址来了解 Gitee 上的优秀开源项目
4.  [GVP](https://gitee.com/gvp) 全称是 Gitee 最有价值开源项目，是综合评定出的优秀开源项目
5.  Gitee 官方提供的使用手册 [https://gitee.com/help](https://gitee.com/help)
6.  Gitee 封面人物是一档用来展示 Gitee 会员风采的栏目 [https://gitee.com/gitee-stars/](https://gitee.com/gitee-stars/)