Feature Selection and Feature Extraction in Pattern Analysis: A Literature Review
It provides a comprehensive overview for researchers and practitioners, but it is incremental as it synthesizes existing literature without introducing new methods.
This paper reviews existing methods for feature selection and extraction in pattern analysis, comparing their theory, applications, and numerical implementations.
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.