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Unsupervised Feature Extraction Applied to Bioinformatics A PCA Based and TD Based Approach /

This updated book proposes applications of tensor decomposition to unsupervised feature extraction and feature selection. The author posits that although supervised methods including deep learning have become popular, unsupervised methods have their own advantages. He argues that this is the case be...

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Bibliografische gegevens
Hoofdauteur: Taguchi, Y-h (Auteur)
Coauteur: SpringerLink (Online service)
Formaat: Elektronisch E-boek
Taal:Engels
Gepubliceerd in: Cham : Springer International Publishing : Imprint: Springer, 2024.
Editie:2nd ed. 2024.
Reeks:Unsupervised and Semi-Supervised Learning,
Onderwerpen:
Telecommunication.
Bioinformatics.
Signal processing.
Pattern recognition systems.
Data mining.
Communications Engineering, Networks.
Computational and Systems Biology.
Signal, Speech and Image Processing.
Automated Pattern Recognition.
Data Mining and Knowledge Discovery.
Online toegang:https://doi.org/10.1007/978-3-031-60982-4
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Inhoudsopgave:
  • Introduction to linear algebra
  • Matrix factorization
  • Tensor decompositions
  • PCA based unsupervised FE
  • TD based unsupervised FE
  • Application of PCA based unsupervised FE to bioinformatics
  • Application of TD based unsupervised FE to bioinformatics
  • Theoretical investigation of TD and PCA based unsupervised FE.

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