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

arXiv: https://arxiv.org/abs/1905.02845 · HTML: https://mlsanity.com/paper/1905.02845/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 May 2019.

## Built on by (most cited first, 15 of 15)

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- [DPDR: A novel machine learning method for the Decision Process for Dimensionality Reduction](https://mlsanity.com/paper/2206.08974/) (2022)
- [Hierarchical Subspace Learning for Dimensionality Reduction to Improve Classification Accuracy in Large Data Sets](https://mlsanity.com/paper/2105.12005/) (2021)
- [Deep-MDS Framework for Recovering the 3D Shape of 2D Landmarks from a Single Image](https://mlsanity.com/paper/2210.15200/) (2022)
- [A Principled Hierarchical Deep Learning Approach to Joint Image Compression and Classification](https://mlsanity.com/paper/2310.19675/) (2023)

## Abstract (arXiv)

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.