Feature Selection and Feature Extraction in Pattern Analysis: A Literature Review
arXiv:1905.02845
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.
14 pages, 1 figure, 2 tables, survey (literature review) paper
References in corpus (5)
- A Review of Feature Selection Methods Based on Mutual Information
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- Eigenvalue and Generalized Eigenvalue Problems: Tutorial
- Pixel-Level Alignment of Facial Images for High Accuracy Recognition Using Ensemble of Patches
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