1 |
Z. Zhao and H. Liu, "Spectral feature selection for supervised and unsupervised learning," in Proceedings of the 24th International Conference on Machine Learning, Corvalis, OR, 2007, pp. 1151-1157.
|
2 |
X. Liu, L. Wang, J. Zhang, J. Yin, and H. Liu, "Global and local structure preservation for feature selection," IEEE Transactions on Neural Networks and Learning Systems, vol. 25, no. 6, pp. 1083-1095, 2014.
DOI
|
3 |
X. Ye, K. Ji, and T. Sakurai, "Global discriminant analysis for unsupervised feature selection with local structure preservation," in Proceedings of the 29th International Florida Artificial Intelligence Research Society Conference, Key Largo, FL, 2016, pp. 454-459.
|
4 |
D. Cai, C. Zhang, and X. He, "Unsupervised feature selection for multi-cluster data," in Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, DC, 2010, pp. 333-342.
|
5 |
C. Hou, F. Nie, D. Yi, and Y. Wu, "Feature selection via joint embedding learning and sparse regression," in Proceedings of International Joint Conference on Artificial Intelligence, Barcelona, Spain, 2011, pp. 1324-1329.
|
6 |
Z. Li, Y. Yang, J. Liu, X. Zhou, and H. Lu, "Unsupervised feature selection using nonnegative spectral analysis," in Proceedings of the 26th AAAI Conference on Artificial Intelligence, Toronto, Canada, 2012, pp. 1026-1032.
|
7 |
Z. Zhang and H. Zha, "Nonlinear dimension reduction via local tangent space alignment," in Proceedings of the International Conference on Intelligent Data Engineering and Automated Learning, Hong Kong, 2003, pp. 477-481.
|
8 |
F. Nie, H. Huang, X. Cai, and C. Ding, "Efficient and robust feature selection via joint l2,1-norms minimization," Advances in Neural Information Processing Systems, vol. 23, pp. 1813-1821, 2010.
|
9 |
T. R. Golub, D. K. Slonim, P. Tamayo, C. Huard, M. Gaasenbeek, J. P. Mesirov, H. Coller, M. L. Loh, J. R. Downing, M. A. Caligiuri, et al., "Molecular classification of cancer: class discovery and class prediction by gene expression monitoring," Science, vol. 286, no. 5439, pp. 531-537, 1999.
DOI
|
10 |
E. De Rinaldis and A. Lahm, DNA Microarrays: Current Applications. Norfolk, UK: Horizon Scientific Press, 2007.
|
11 |
R. L. Somorjai, B. Dolenko, and R. Baumgartner, "Class prediction and discovery using gene microarray and proteomics mass spectroscopy data: curses, caveats, cautions," Bioinformatics, vol. 19, no. 12, pp. 1484-1491, 2003.
DOI
|
12 |
P. Zhu, W. Zuo, L. Zhang, Q. Hu, and S. C. Shiu, "Unsupervised feature selection by regularized self-representation," Pattern Recognition, vol. 48, no. 2, pp. 438-446, 2015.
DOI
|
13 |
I. Guyon, J. Weston, S. Barnhill, and V. Vapnik, "Gene selection for cancer classification using support vector machines," Machine Learning, vol. 46, no. 1, pp. 389-422, 2002.
DOI
|
14 |
J. G. Dy and C. E. Brodley, "Visualization and interactive feature selection for unsupervised data," in Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Boston, MA, 2000, pp. 360-364.
|
15 |
S. Zhang, H. S. Wong, Y. Shen, and D. Xie, "A new unsupervised feature ranking method for gene expression data based on consensus affinity," IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), vol. 9, no. 4, pp. 1257-1263, 2012.
DOI
|
16 |
X. Ye, K. Ji, and T. Sakurai, "Unsupervised feature selection with correlation and individuality analysis," International Journal of Machine Learning and Computing, vol. 6, no. 1, pp. 36-41, 2016.
|
17 |
X. He, D. Cai, and P. Niyogi, "Laplacian score for feature selection," Advances in Neural Information Processing Systems, vol. 18, pp. 507-514, 2006.
|