The Re-implementation of Feature Selection & Clustering Methods

This repository integrates the codes for some feature selection, clustering and subspace learning methods. I know how hard it is to reproduce the codes, especially for beginners, and I hope it can help.

• If you find any errors or need any help in reproducing the code, please feel free to contact me.

• If any author or publisher has questions, please contact me to remove or replace them.

• My email is coding495@163.com

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To run supervised method:

'Demo_Supervised.m' gives a simple example for supervised methods.

To run the codes, the size of the inputs are: , where m is the dimension, and and present the number of the training and test samples, respectively ( is used to calculate the clustering results, and is not involved in training).

To run unsupervised method:

'Demo_Unsupervised.m' gives a simple example for unsupervised methods.

To run the codes, the size of the inputs are: , where m is the dimension, and presents the number of the training samples ( is used to calculate the clustering results, and is not involved in training).

The codes exist in the repository

1. Feature Selection

1.1 Supervised Methods

The codes will be available soon.

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1.2 Unsupervised Methods

• 2019-LRLMR [1]: Unsupervised feature selection via latent representation learning and manifold regularization.

  We reproduce the codes as same as the descriptions of the paper. The official codes are available at http://tangchang.net/

• 2019-URAFS [2]: Generalized Uncorrelated Regression with Adaptive Graph for Unsupervised Feature Selection.

• 2021-AGUFS [3]: Adaptive graph-based generalized regression model for unsupervised feature selection.

• 2021-DSLRL [4]: Dual space latent representation learning for unsupervised feature selection.

• 2022-DLUFS [5]: Low-rank dictionary learning for unsupervised feature selection.

  The official codes (python implementation) are available at https://github.com/mohsengh/DLUFS/

• 2022-SLMEA [6]: Sparse and low-dimensional representation with maximum entropy adaptive graph for feature selection.

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2. Clustering

• 2015-rLPP [13]: Learning Robust Locality Preserving Projection via p-Order Minimization.

3. Representation & Subspace Learning

3.1 Supervised Methods

• 2017-MRSL [9]: Marginal Representation Learning With Graph Structure Self-Adaptation.

  Official codes are available at https://github.com/DarrenZZhang/MSRL.

• 2019-RSLDA [7]: Robust Sparse Linear Discriminant Analysis.

• 2020-LRDAGP [10]: Low-Rank Discriminative Adaptive Graph Preserving Subspace Learning.

• 2020-RDA_FSIS [12]: Linear embedding by joint Robust Discriminant Analysis and Inter-class Sparsity.

• 2021-SN-TSL [11] :Sparse non-negative transition subspace learning for image classification.

• 2021-DSDPL [8]: Dual subspace discriminative projection learning.

3.2 Unsupervised Methods

• 2020-JLRSL [14]: Joint low-rank representation and spectral regression for robust subspace learning.

Reference

[1] Tang, Chang, et al. "Unsupervised feature selection via latent representation learning and manifold regularization." Neural Networks 117 (2019): 163-178.

[2] X. Li, H. Zhang, R. Zhang, Y. Liu and F. Nie, "Generalized Uncorrelated Regression with Adaptive Graph for Unsupervised Feature Selection," in IEEE Transactions on Neural Networks and Learning Systems, vol. 30, no. 5, pp. 1587-1595, May 2019, doi: 10.1109/TNNLS.2018.2868847.

[3] Huang, Yanyong, et al. "Adaptive graph-based generalized regression model for unsupervised feature selection." Knowledge-Based Systems 227 (2021), doi: 10.1016/j.knosys.2021.107156.

[4] Shang, Ronghua, et al. "Dual space latent representation learning for unsupervised feature selection." Pattern Recognition 114 (2021), doi: 10.1016/j.patcog.2021.107873.

[5] Parsa, Mohsen Ghassemi, Hadi Zare, and Mehdi Ghatee. "Low-rank dictionary learning for unsupervised feature selection." Expert Systems with Applications 202 (2022), doi: 10.1016/j.eswa.2022.117149.

[6] Shang, Ronghua, et al. "Sparse and low-dimensional representation with maximum entropy adaptive graph for feature selection." Neurocomputing 485 (2022): 57-73.

[7] J. Wen et al., "Robust Sparse Linear Discriminant Analysis," in IEEE Transactions on Circuits and Systems for Video Technology, vol. 29, no. 2, pp. 390-403, Feb. 2019, doi: 10.1109/TCSVT.2018.2799214.

[8] Belous, Gregg, Andrew Busch, and Yongsheng Gao. "Dual subspace discriminative projection learning." Pattern Recognition 111 (2021), doi: 10.1016/j.patcog.2020.107581.

[9] Zhang, Zheng, et al. "Marginal representation learning with graph structure self-adaptation." IEEE Transactions on Neural Networks and Learning Systems 29.10 (2017): 4645-4659.

[10] Du, Haishun, et al. "Low-rank discriminative adaptive graph preserving subspace learning." Neural Processing Letters 52.3 (2020): 2127-2149.

[11] Chen, Zhe, et al. "Sparse non-negative transition subspace learning for image classification." Signal Processing 183 (2021), doi: 10.1016/j.sigpro.2021.107988.

[12] Dornaika, Fadi, and A. Khoder. "Linear embedding by joint robust discriminant analysis and inter-class sparsity." Neural Networks 127 (2020): 141-159.

[13] Wang, Hua, Feiping Nie, and Heng Huang. "Learning robust locality preserving projection via p-order minimization." In Twenty-Ninth AAAI Conference on Artificial Intelligence. 2015.

[14] Peng, Yong, Leijie Zhang, Wanzeng Kong, Feiwei Qin, and Jianhai Zhang. "Joint low-rank representation and spectral regression for robust subspace learning." Knowledge-Based Systems 195 (2020), doi: 10.1016/j.knosys.2020.105723.