Code
The following Matlab codes except feature selection based on structured sparsity are written by me. If you have questions or find bugs in the codes, feel free to contact me. If you find these codes useful, we appreciate it very much if you can cite our related paper in: https://guijiejie.github.io/publications.html or https://scholar.google.com/citations?user=f8oE8NgAAAAJ&hl=en.
Classification
A unified framework for classical classifiers
J. Gui*, T. Liu, Z. Sun, D. Tao, and T. Tan, "Representative Vector Machines: A unified framework for classical classifiers", IEEE Transactions on Cybernetics, vol. 46, no. 8, pp. 1877-1888, 2016.
Nearest neighbor classifier (NN)
T. Cover and P. Hart, “Nearest neighbor pattern classification,” IEEE Trans. Inf. Theory, vol. 13, no. 1, pp. 21–27, Jan. 1967. [Code]
Nearest feature line classifier (NFL)
S. Li and J. Lu, “Face recognition using the nearest feature line method,” IEEE Trans. Neural Netw., vol. 10, no. 2, pp. 439–443, Mar. 1999. [Code]
Nearest feature plane classifier (NFP)
J. Chien and C. Wu, “Discriminant waveletfaces and nearest feature classifiers for face recognition,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 12, pp. 1644–1649, Dec. 2002. [Code]
Nearest feature space classifier (NFS)
I. Naseem, R. Togneri, and M. Bennamoun, “Linear regression for face recognition,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 32, no. 11, pp. 2106–2112, Nov. 2010. [Code]
Nearest centroid classifier (NC)
Shuiwang Ji and Jieping Ye. Generalized linear discriminant analysis: a unified framework and efficient model selection. IEEE Transactions on Neural Networks, 19(10):1768-1782, 2008. [Code]
Subspace learning
Jieping Ye, Qi Li, Hui Xiong, Haesun Park, Ravi Janardan, and Vipin Kumar. IDR/QR: an incremental dimension reduction algorithm via qr decomposition. IEEE Transactions on Knowledge and Data Engineering, 17(9):1208-1222, 2005. [Code]
Jieping Ye and Qi Li. A two-stage linear discriminant analysis via QR-decomposition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(6):929-941, 2005. [Code]
Feature selection based on structured sparsity
C. Hou, F. Nie, X. Li, D. Yi, and Y. Wu, “Joint embedding learning and sparse regression: A framework for unsupervised feature selection,” IEEE Trans. Cybern., vol. 44, no. 6, pp. 793–804, Jun. 2014. [Code]
X. Cai, F. Nie, and H. Huang, “Exact top-k feature selection via l2,0-norm constraint,” in Proc. Int. Joint Conf. Artif. Intell., 2013, pp. 1240–1246. [Code]
S. Xiang, F. Nie, G. Meng, C. Pan, and C. Zhang, “Discriminative least squares regression for multiclass classification and feature selection,” IEEE Trans. Neural Netw. Learn. Syst., vol. 23, no. 11, pp. 1738–1754, Nov. 2012. [Code]
R. He, T. Tan, L. Wang, and W. S. Zheng, “l2,1 regularized correntropy for robust feature selection,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2012, pp. 2504–2511. [Code]
Y. Yang, H. T. Shen, Z. Ma, Z. Huang, and X. Zhou, “l2,1-norm regularized discriminative feature selection for unsupervised learning,” in Proc. 22nd Int. Joint Conf. Artif. Intell., 2011, pp. 1589–1594. [Code]
F. Nie, H. Huang, X. Cai, and C. Ding, “Efficient and robust feature selection via joint l2,1-norms minimization,” in Proc. Adv. Neural Inf. Process. Syst., 2010, pp. 1813–1821. [Code]
A. Destrero, C. De Mol, F. Odone, and A. Verri, “A sparsity-enforcing method for learning face features,” IEEE Trans. Image Process., vol. 18, no. 1, pp. 188–201, Jan. 2009. [Code]
Some other useful documents about Matlab
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