Dimensionality Reduction: Challenges and Solutions

The use of dimensionality reduction techniques is a keystone for analyzing and interpreting high dimensional data. These techniques gather several data features of interest, such as dynamical structure, input-output relationships, the correlation between data sets, covariance, etc. Dimensionality reduction entails mapping a set of high dimensional data features onto low dimensional data. Motivated by the lack of learning models’ performance due to the high dimensionality data, this study encounters five distinct dimensionality reduction methods. Besides, a comparison between reduced dimensionality data and the original one using statistical and machine learning models is conducted thoroughly.

• Publication year

  2022

• Venue

  ITM Web of Conferences

• Publication date

  Unknown publication date

• Fields of study

  Not labeled

• Identifiers
  DOI 10.1051/itmconf/20224301017
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

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• No concepts are published for this paper.

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