Feature Selection and Feature Extraction in Pattern Analysis: A Literature Review

TL;DR

This literature review discusses various feature selection and extraction methods used in pattern analysis, highlighting their theoretical foundations, applications, numerical implementations, and comparative evaluations.

Contribution

It provides a comprehensive overview of existing feature selection and extraction techniques, including theoretical insights, practical applications, and comparative analysis.

Findings

Different methods have unique advantages and limitations.

Numerical implementations demonstrate practical applicability.

Comparison helps in selecting suitable methods for specific tasks.

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.