Kent feature embedding for classification of compositional data with zeros-Reference-Cited by-同舟云学术

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Kent feature embedding for classification of compositional data with zeros

Published:2024-01-31 Issue:2 Volume:34 Page:
ISSN:0960-3174
Container-title:Statistics and Computing
language:en
Short-container-title:Stat Comput

Author:

Lu Shan,Wang Wenjing,Guan Rong

Funder

Jing Ying Scholar Support Program in Central University of Finance and Economics

Program for Innovation Research in Central University of Finance and Economics

“Double First-Class” Disciplinary Project in Central University of Finance and Economics

Disciplinary Funding in Central University of Finance and Economics

National Natural Science Foundation of China

General Project of Humanities and Social Sciences

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s11222-024-10382-z.pdf

Reference55 articles.

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2. Armanfard, N., Reilly, J.P., Komeili, M.: Local feature selection for data classification. IEEE Trans. Pattern Anal. Mach. Intell. 38(6), 1217–1227 (2016). https://doi.org/10.1109/TPAMI.2015.2478471

3. Bello, M., Nápoles, G., Vanhoof, K., Bello, R.: Data quality measures based on granular computing for multi-label classification. Inf. Sci. 560, 51–67 (2021). https://doi.org/10.1016/j.ins.2021.01.027

4. Cuesta-Albertos, J.A., Cuevas, A., Fraiman, R.: On projection-based tests for directional and compositional data. Stat. Comput. 19(4), 367 (2009). https://doi.org/10.1007/s11222-008-9098-3

5. Fan, J., Feng, Y., Jiang, J., Tong, X.: Feature augmentation via nonparametrics and selection (fans) in high-dimensional classification. J. Am. Stat. Assoc. 111(513), 275–287 (2016). https://doi.org/10.1080/01621459.2015.1005212

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