A review of unsupervised feature selection methods
By Saúl Solorio-Fernández, J. A. Carrasco-Ochoa, José Fco. Martínez-Trinidad. Artificial Intelligence Review, 2019
553 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/ea50aee7f76012cf9cfd5f5b7021b7fb39a7b9a1
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Ayça Deniz
- Arman Malekloo
- Tansel Dökeroğlu
- Hakan Ezgi Kiziloz
- Mark A. Girolami
- Ekin Ozer
- Mohammad AlHamaydeh
- Chris J. Oates
In Computer Vision and Pattern Recognition
- Ayça Deniz
- Arman Malekloo
- Tansel Dökeroğlu
- Hakan Ezgi Kiziloz
- Mark A. Girolami
- Ekin Ozer
- Mohammad AlHamaydeh
- Francesco Camastra
Citations in the corpus
- Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights (2021, methodology): “A recent review of unsupervised feature selection methods is provided by Solorio-Fern´andez et al. 174 In the context of SHM and ML, Bull et al. 175 utilized an unsupervised feature selection procedure in ensemble analysis, speci fi cally bagging and feature bagging.”
- A comprehensive survey on recent metaheuristics for feature selection (2022, background): “provided a comprehensive review of unsupervised feature selection methods [83].”