Download PDFOpen PDF in browser

An Evaluation of Strategies for Dimensionality Reduction

10 pagesPublished: 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.

BibTeX entry
@inproceedings{CAINE2023:Evaluation_Strategies_Dimensionality_Reduction,
  author    = {Gautam Singh},
  title     = {An Evaluation of Strategies for Dimensionality Reduction},
  booktitle = {Proceedings of 36th International Conference on Computer Applications in Industry and Engineering},
  editor    = {Krishna Kambhampaty and Gongzhu Hu and Indranil Roy},
  series    = {EPiC Series in Computing},
  volume    = {97},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {/publications/paper/TwRK},
  doi       = {10.29007/gzx4},
  pages     = {81-90},
  year      = {2024}}
Download PDFOpen PDF in browser