Feature Dimensionality Reduction via Homological Properties of Observability]{Feature Dimensionality Reduction via Homological Properties of Observability

MARCELLO TROVATI, Eslam Farsimadan

Research output: Contribution to journalArticle (journal)peer-review

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Abstract

Feature selection and its subsequent dimensionality reduction are significant problems in machine learning and it is at the core of several data science techniques. The ‘shape’ of data, or in other words its related topological properties, can provide crucial insights into the corresponding data types and sources and it enables the identification of general properties that facilitate its analysis and assessment. In this article, we discuss an information theoretic approach combined with data homological properties to assess dimensionality reduction, which can be applied to semantic feature selection.
Original languageEnglish
Pages (from-to)1-7
JournalEvolving Systems
Early online date30 Aug 2023
DOIs
Publication statusPublished - 30 Aug 2023

Keywords

  • Information theory
  • Applied homology
  • Dimensionality reduction

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