The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
Nonparametric weighted feature extraction for classification
Abstract
In this paper, a new nonparametric feature extraction method is proposed for high-dimensional multiclass pattern recognition problems. It is based on a nonparametric extension of scatter matrices. There are at least two advantages to using the proposed nonparametric scatter matrices. First, they are generally of full rank. This provides the ability to specify the number of extracted features desired and to reduce the effect of the singularity problem. This is in contrast to parametric discriminant analysis, which usually only can extract L-1 (number of classes minus one) features. In a real situation, this may not be enough. Second, the nonparametric nature of scatter matrices reduces the effects of outliers and works well even for nonnormal datasets. The new method provides greater weight to samples near the expected decision boundary. This tends to provide for increased classification accuracy.
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Cites
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Cited by
Ghamisi, P., Yokoya, N., Li, J., Liao, W., Liu, S., Plaza, J., Rasti, B., & Plaza, A. (2017) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2017.2762087 · Cited by 909
Hyperspectral Image Classification—Traditional to Deep Models: A Survey for Future Prospects
Ahmad, M., Shabbir, S., Roy, S. K., Hong, D., Wu, X., Yao, J., Khan, A. M., Mazzara, M., Distefano, S., & Chanussot, J. (2022) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2021.3133021 · Cited by 438
Ghamisi, P., Maggiori, E., Li, S., Souza, R., Tarablaka, Y., Moser, G., De Giorgi, A., Fang, L., Chen, Y., Chi, M., et al. (2018) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2018.2854840 · Cited by 325
Imani, M., & Ghassemian, H. (2020) · Information Fusion · doi:10.1016/j.inffus.2020.01.007 · Cited by 319
Metadata
- Authors
- Kuo, B.-C., & Landgrebe, D. A.
- Venue
- IEEE Transactions on Geoscience and Remote Sensing
- Published
- 2004-05
- Volume
- 42
- Issue
- 5
- Pages
- 1096-1105
- ISSN
- 0196-2892 (ISSN-L)
- OpenAlex
- W2118796925
- Cited by
- 425
- References
- 15
- FWCI
- 10.83
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
