Year: 2019
Type: preprint
Abstract: Pattern analysis often requires a pre-processing stage for extracting or selecting features in order to help the classification, prediction, or clustering stage discriminate or represent the data in a better way. The reason for this requirement is that the raw data are complex and difficult to process without extracting or selecting appropriate features beforehand. This paper reviews theory and motivation of different common methods of feature selection and extraction and introduces some of their applications. Some numerical implementations are also shown for these methods. Finally, the methods in feature selection and extraction are compared.
Source: arXiv (Cornell University)
Authors: Ghojogh, Benyamin, Samad, Maria N., Mashhadi, Sayema Asif, Kapoor, Tania, Ali, Wahab, Karray, Fakhri, Crowley, Mark
Institution: University of Waterloo
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2943848461