An Evaluation of Strategies for Dimensionality Reduction

10 pages•Published: January 24, 2024

Abstract

The “curse of dimensionality” in machine learning refers to the increasing data training requirements for features collected from high-dimensional spaces. Researchers generally use one of several dimensionality reduction methods to visualize data and estimate data trends. Feature engineering and selection minimize dimensionality and optimize algorithms. Di- mensionality must be matched to the data to preserve information. This paper compares the final model evaluation dimensionality reduction methods. First, encode the data set in a smaller dimension to avoid the curse of dimensionality and train the model with a manageable number of features.

Keyphrases: curse of dimensionality, dimensionality reduction, high dimensional data, machine learning, model evaluation.

In: Krishna Kambhampaty, Gongzhu Hu and Indranil Roy (editors). Proceedings of 36th International Conference on Computer Applications in Industry and Engineering, vol 97, pages 81-90.

Links:	https://easychair.org/publications/paper/TwRK
	https://doi.org/10.29007/gzx4