# Performance analysis of unsupervised feature selection methods

arXiv: https://arxiv.org/abs/1306.1326 · HTML: https://mlsanity.com/paper/1306.1326/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jun 2013.

## Built on by (most cited first, 1 of 1)

- [A Notion of Feature Importance by Decorrelation and Detection of Trends by Random Forest Regression](https://mlsanity.com/paper/2303.01156/) (2023)

## Abstract (arXiv)

Feature selection (FS) is a process which attempts to select more informative features. In some cases, too many redundant or irrelevant features may overpower main features for classification. Feature selection can remedy this problem and therefore improve the prediction accuracy and reduce the computational overhead of classification algorithms. The main aim of feature selection is to determine a minimal feature subset from a problem domain while retaining a suitably high accuracy in representing the original features. In this paper, Principal Component Analysis (PCA), Rough PCA, Unsupervised Quick Reduct (USQR) algorithm and Empirical Distribution Ranking (EDR) approaches are applied to discover discriminative features that will be the most adequate ones for classification. Efficiency of the approaches is evaluated using standard classification metrics.