The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Local-Learning-Based Feature Selection for High-Dimensional Data Analysis
Open accessAbstract
This paper considers feature selection for data classification in the presence of a huge number of irrelevant features. We propose a new feature-selection algorithm that addresses several major issues with prior work, including problems with algorithm implementation, computational complexity, and solution accuracy. The key idea is to decompose an arbitrarily complex nonlinear problem into a set of locally linear ones through local learning, and then learn feature relevance globally within the large margin framework. The proposed algorithm is based on well-established machine learning and numerical analysis techniques, without making any assumptions about the underlying data distribution. It is capable of processing many thousands of features within minutes on a personal computer while maintaining a very high accuracy that is nearly insensitive to a growing number of irrelevant features. Theoretical analyses of the algorithm's sample complexity suggest that the algorithm has a logarithmical sample complexity with respect to the number of features. Experiments on 11 synthetic and real-world data sets demonstrate the viability of our formulation of the feature-selection problem for supervised learning and the effectiveness of our algorithm.
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Citations
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
Gene selection for cancer classification using support vector machines
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Gene expression profiling predicts clinical outcome of breast cancer
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A Probabilistic Theory of Pattern Recognition
Devroye, L., Györfi, L., & Lugosi, G. (1996) · Stochastic Modelling and Applied Probability · doi:10.1007/978-1-4612-0711-5 · Cited by 3,274
Feature selection, L 1 vs. L 2 regularization, and rotational invariance
Ng, A. Y. (2004) · Twenty-first international conference on Machine learning - ICML '04 · doi:10.1145/1015330.1015435 · Cited by 1,572
Cited by
Relief-based feature selection: Introduction and review
Urbanowicz, R. J., Meeker, M., La Cava, W., Olson, R. S., & Moore, J. H. (2018) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2018.07.014 · Cited by 1,311
Novel Speech Signal Processing Algorithms for High-Accuracy Classification of Parkinson’s Disease
Tsanas, A., Little, M. A., McSharry, P. E., Spielman, J., & Ramig, L. O. (2012) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2012.2183367 · Cited by 732
Recent advances in feature selection and its applications
Li, Y., Li, T., & Liu, H. (2017) · Knowledge and Information Systems · doi:10.1007/s10115-017-1059-8 · Cited by 400
Document clustering: TF-IDF approach
Bafna, P., Pramod, D., & Vaidya, A. (2016) · 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT) · doi:10.1109/iceeot.2016.7754750 · Cited by 290
Vivekanandan, T., & Sriman Narayana Iyengar, N. C. (2017) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2017.09.011 · Cited by 185
Feature Selection Boosted by Unselected Features
Zheng, W., Chen, S., Fu, Z., Zhu, F., Yan, H., & Yang, J. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3058172 · Cited by 51
Brahmi, Z., Mahyoob, M., Al-Sarem, M., Algaraady, J., Bousselmi, K., & Alblwi, A. (2024) · Psychology Research and Behavior Management · doi:10.2147/prbm.s460283 · Cited by 36
Talk2Me: Automated linguistic data collection for personal assessment
Komeili, M., Pou-Prom, C., Liaqat, D., Fraser, K. C., Yancheva, M., & Rudzicz, F. (2019) · PLOS ONE · doi:10.1371/journal.pone.0212342 · Cited by 23
Metadata
- Authors
- Sun, Y., Todorovic, S., & Goodison, S.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2010-09
- Volume
- 32
- Issue
- 9
- Pages
- 1610-1626
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2148830179
- Cited by
- 347
- References
- 5
- FWCI
- 16.86
- Type
- journal article
- Language
- en
- Open access
- green
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