The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Supervised feature selection via dependence estimation
Open accessAbstract
We introduce a framework for filtering features that employs the Hilbert-Schmidt Independence Criterion (HSIC) as a measure of dependence between the features and the labels. The key idea is that good features should maximise such dependence. Feature selection for various supervised learning problems (including classification and regression) is unified under this framework, and the solutions can be approximated using a backward-elimination algorithm. We demonstrate the usefulness of our method on both artificial and real world datasets.
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Cites
Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482
The Elements of Statistical Learning
Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162
Smyth, G. K. (2004) · Statistical Applications in Genetics and Molecular Biology · doi:10.2202/1544-6115.1027 · Cited by 11,829
Gene selection for cancer classification using support vector machines
Guyon, I., Weston, J., Barnhill, S., & Vapnik, V. (2002) · Machine Learning · doi:10.1023/a:1012487302797 · Cited by 9,954
Gene expression profiling predicts clinical outcome of breast cancer
van 't Veer, L. J., Dai, H., van de Vijver, M. J., He, Y. D., Hart, A. A. M., Mao, M., Peterse, H. L., van der Kooy, K., Marton, M. J., Witteveen, A. T., et al. (2002) · Nature · doi:10.1038/415530a · Cited by 9,536
Integrating structured biological data by Kernel Maximum Mean Discrepancy
Borgwardt, K. M., Gretton, A., Rasch, M. J., Kriegel, H.-P., Schölkopf, B., & Smola, A. J. (2006) · Bioinformatics · doi:10.1093/bioinformatics/btl242 · Cited by 1,674
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Cited by
Kernel Mean Embedding of Distributions: A Review and Beyond
Muandet, K., Fukumizu, K., Sriperumbudur, B., & Schölkopf, B. (2017) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000060 · Cited by 409
Efficient Spectral Feature Selection with Minimum Redundancy
Zhao, Z., Wang, L., & Liu, H. (2010) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v24i1.7671 · Cited by 277
Incomplete multi-modal representation learning for Alzheimer’s disease diagnosis
Liu, Y., Fan, L., Zhang, C., Zhou, T., Xiao, Z., Geng, L., & Shen, D. (2021) · Medical Image Analysis · doi:10.1016/j.media.2020.101953 · Cited by 102
A survey on online feature selection with streaming features
Hu, X., Zhou, P., Li, P., Wang, J., & Wu, X. (2018) · Frontiers of Computer Science · doi:10.1007/s11704-016-5489-3 · Cited by 88
Massively parallel feature selection: an approach based on variance preservation
Zhao, Z., Zhang, R., Cox, J., Duling, D., & Sarle, W. (2013) · Machine Learning · doi:10.1007/s10994-013-5373-4 · Cited by 85
Metadata
- Authors
- Song, L., Smola, A., Gretton, A., Borgwardt, K. M., & Bedo, J.
- Venue
- Proceedings of the 24th international conference on Machine learning
- Publisher
- ACM
- Published
- 2007-06-20
- Pages
- 823-830
- OpenAlex
- W2101267652
- Cited by
- 379
- References
- 6
- FWCI
- 35.36
- Type
- conference paper
- Language
- en
- Open access
- green
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