Feature selection aims to find a set of features that are concise and have good generalization capabilities by removing redundant, uncorrelated, and noisy features. Recently, the regularized self-representation (RSR) method was proposed for unsupervised feature selection by minimizing the L2,1 norm of residual matrix and self-representation coefficient matrix. In this paper, we find that minimizing the L2,1 norm of the self-representation coefficient matrix cannot effectively extract the features with strong correlation. Therefore, by adding the minimum constraint on the kernel norm of the self-representation coefficient matrix, a new unsupervised feature selection method named low-rank regularized self-representation (LRRSR) is proposed, which can effectively discover the overall structure of the data. Experiments show that the proposed algorithm has better performance on clustering tasks than RSR and other related algorithms.
Cite this paper
Li, W. and Wei, L. (2020). Unsupervised Feature Selection Based on Low-Rank Regularized Self-Representation. Open Access Library Journal, 7, e6274. doi: http://dx.doi.org/10.4236/oalib.1106274.
Zhu, X., Zhang, S., Jin, Z., Zhang, Z. and Xu, Z. (2011) Missing Value Estimation for Mixed-Attribute Data Sets. IEEE Transactions on Knowledge and Data Engineering, 23, 110-121. https://doi.org/10.1109/TKDE.2010.99
Zheng, M., Bu, J., Chen, C., Wang, C., Zhang, L., Qiu, G. and Cai, D. (2011) Graph Regularized Sparse Coding for Image Representation. IEEE Transactions on Image Processing, 20, 1327-1336. https://doi.org/10.1109/TIP.2010.2090535
Ma, Z., Yang, Y., Sebe, N. and Hauptmann, A.G. (2014) Knowledge Adaptation with Partially Shared Features for Event Detection Using Few Exemplars. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36, 1789-1802.
https://doi.org/10.1109/TPAMI.2014.2306419
Cai, D., Zhang, C. and He, X. (2010) Unsupervised Feature Selection for Multi-Cluster Data. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD Press, Washington DC, 333-342. https://doi.org/10.1145/1835804.1835848
Gao, S., Tsang, I.W.-H., Chia, L.-T. and Zhao, P. (2010) Local Features Are Not Lonely—Laplacian Sparse Coding for Image Classification. In: The Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition, IEEE Press, San Francisco, 3555-3561. https://doi.org/10.1109/CVPR.2010.5539943
Li, J., Cheng, K., Wang, S., Morstatter, F., Trevino, R.P., Tang, J. and Liu, H. (2018) Feature Selection: A Data Perspective. ACM Computing Surveys, 50, 94.
https://doi.org/10.1145/3136625
He, X., Cai, D. and Niyogi, P. (2005) Laplacian Score for Feature Selection. In: International Conference on Neural Information Processing Systems, MIT Press, Cambridge, 507-514.
Zhao, Z., Wang, L. and Liu, H. (2010) Efficient Spectral Feature Selection with Minimum Redundancy. Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence, AAAI Press, Atlanta, 673-678.
Zhang, S., Zhou, H., Jiang, F. and Li, X. (2015) Robust Visual Tracking Using Structurally Random Projection and Weighted Least Squares. IEEE Transactions on Circuits and Systems for Video Technology, 25, 1749-1760.
https://doi.org/10.1109/TCSVT.2015.2406194
Abualigah, L.M. and Khader, A.T. (2017) Unsupervised Text Feature Selection Technique Based on Hybrid Particle Swarm Optimization Algorithm with Genetic Operators for the Text Clustering. The Journal of Supercomputing, 73, 4773-4795.
https://doi.org/10.1007/s11227-017-2046-2
Han, K., Wang, Y., Zhang, C., Li, C. and Xu, C. (2018) Autoencoder Inspired Unsupervised Feature Selection. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, 15-20 April 2018, 2941-2945.
https://doi.org/10.1109/ICASSP.2018.8462261
Kira, K. and Rendell, L.A. (1992) The Feature Selection Problem: Traditional Methods and a New Algorithm. In: Proceedings of the 10th National Conference on Artificial Intelligence, AAAI Press, Atlanta, 129-134.
Cai, J.-F., Candes, E.J. and Shen, Z. (2010) A Singular Value Thresholding Algorithm for Matrix Completion. SIAM Journal on Optimization, 20, 1956-1982.
https://doi.org/10.1137/080738970
Liu, G., Lin, Z., Yan, S., Sun, J., Yu, Y. and Ma, Y. (2012) Robust Recovery of Subspace Structures by Low-Rank Representation. IEEE Transactions on Software Engineering, 35, 171-184. https://doi.org/10.1109/TPAMI.2012.88
