# Using Dimension Reduction to Improve the Classification of High-dimensional Data

arXiv: https://arxiv.org/abs/1505.06907 · HTML: https://mlsanity.com/paper/1505.06907/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 May 2015.

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

- [Assessing Low Back Movement with Motion Tape Sensor Data Through Deep Learning](https://mlsanity.com/paper/2602.11465/) (2026)

## Abstract (arXiv)

In this work we show that the classification performance of high-dimensional structural MRI data with only a small set of training examples is improved by the usage of dimension reduction methods. We assessed two different dimension reduction variants: feature selection by ANOVA F-test and feature transformation by PCA. On the reduced datasets, we applied common learning algorithms using 5-fold cross-validation. Training, tuning of the hyperparameters, as well as the performance evaluation of the classifiers was conducted using two different performance measures: Accuracy, and Receiver Operating Characteristic curve (AUC). Our hypothesis is supported by experimental results.