[1] Y. Li, C. Luo, and S. M. Chung, "Text clustering with feature selection by using statistical data," IEEE Trans. Knowl. Data Eng., vol. 20, no. 5, pp. 641-652, 2008.
[2] C. He, K. Li, Y. Zhang, Y. Zhang, Z. Guo, X. Li, M. Danelljan, and F. Yu, "Strategic preys make acute predators: Enhancing camouflaged object detectors by generating camouflaged objects," arXiv preprint arXiv:2308.03166, 2023.
[3] H.-J. Yu and D.-S. Huang, "Normalized feature vectors: A novel alignment-free sequence comparison method based on the numbers of adjacent amino acids," IEEE/ACM Trans. Comput. Biol. Bioinformatics, vol. 10, no. 2, pp. 457-467, 2013.
[4] C. He, K. Li, Y. Zhang, L. Tang, Y. Zhang, Z. Guo, and X. Li, "Camouflaged object detection with feature decomposition and edge reconstruction," in Proc. CVPR, 2023, pp. 22 046-22 055.
[5] Z. Sun, G. Bebis, and R. Miller, "Object detection using feature subset selection," Pattern Recognit., vol. 37, no. 11, pp. 2165-2176, 2004.
[6] L. Xu, R. Wang, F. Nie, and X. Li, "Efficient top-k feature selection using coordinate descent method," in Proc. AAAI, vol. 37, no. 9, 2023, pp. 10594-10601.
[7] C. He, K. Li, Y. Zhang, G. Xu, L. Tang, Y. Zhang, Z. Guo, and X. Li, "Weakly-supervised concealed object segmentation with sam-based pseudo labeling and multi-scale feature grouping," arXiv preprint arXiv:2305.11003, 2023.
[8] M. F. Balin, A. Abid, and J. Zou, "Concrete autoencoders: Differentiable feature selection and reconstruction," in Proc. ICML, vol. 97, 2019, pp. 444-453.
[9] K. Han, Y. Wang, C. Zhang, C. Li, and C. Xu, "Autoencoder inspired unsupervised feature selection," in Proc. ICASSP, 2018, pp. 2941-2945.
[10] M. McPherson, L. Smith-Lovin, and J. M. Cook, "Birds of a feather: Homophily in social networks," Annu. Rev. Sociol., vol. 27, no. 1, pp. 415-444, 2001.
[11] D. I. Shuman, S. K. Narang, P. Frossard, A. Ortega, and P. Vandergheynst, "The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains," IEEE Signal Process. Mag., vol. 30, no. 3, pp. 83-98, 2013.
[12] R. Wang, P. Wang, D. Wu, Z. Sun, F. Nie, and X. Li, "Multi-view and multi-order structured graph learning," IEEE Trans. Neural Netw. Learn. Syst., pp. 1-12, 2023.
[13] X. He, D. Cai, and P. Niyogi, “Laplacian score for feature selection,” in Proc. NIPS, 2005, pp. 507-514.
[14] O. Lindenbaum, U. Shaham, E. Peterfreund, J. Svirsky, N. Casey, and Y. Kluger, "Differentiable unsupervised feature selection based on a gated laplacian," in Proc. NIPS, vol. 34, 2021, pp. 1530-1542.
[15] U. Von Luxburg, “A tutorial on spectral clustering,” Stat. Comput., vol. 17, pp. 395–416, 2007.
[16] F. R. Chung, Spectral graph theory. American Mathematical Soc., 1997, vol. 92.
[17] E. Jang, S. Gu, and B. Poole, “Categorical reparameterization with gumbel-softmax,” arXiv preprint arXiv:1611.01144, 2017.
[18] C. J. Maddison, A. Mnih, and Y. W. Teh, "The concrete distribution: A continuous relaxation of discrete random variables," in Proc. ICLR, 2017.
[19] A. Tikhonov and V. Arsenin, *Solutions of Ill-posed Problems*. Winston, 1977.
[20] S. P. Boyd and L. Vandenberghe, Convex Optimization. Cambridge University Press, 2014.
[21] F. Nie, X. Wang, M. Jordan, and H. Huang, "The constrained laplacian rank algorithm for graph-based clustering," in Proc. AAAI, vol. 30, no. 1, 2016.