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

Y-h. Taguchi

ISBN 10: 3031609816 / ISBN 13: 9783031609817

Edité par Springer International Publishing, Springer International Publishing, 2024

A propos de cet article

Druck auf Anfrage Neuware - Printed after ordering - 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 because unsupervised methods are easy to learn since tensor decomposition is a conventional linear methodology. This book starts from very basic linear algebra and reaches the cutting edge methodologies applied to difficult situations when there are many features (variables) while only small number of samples are available. The author includes advanced descriptions about tensor decomposition including Tucker decomposition using high order singular value decomposition as well as higher order orthogonal iteration, and train tensor decomposition. The author concludes by showing unsupervised methods and their application to a wide range of topics.

N° de réf. du vendeur 9783031609817

Détails bibliographiques

• Titre: Unsupervised Feature Extraction Applied to Bioinformatics : A PCA Based and TD Based Approach
• Éditeur: Springer International Publishing, Springer International Publishing
• Date de parution: 2024
• Langue: anglais
• ISBN à 10 chiffres: 3031609816
• ISBN à 13 chiffres: 9783031609817
• Reliure: Buch
• Édition: 2ème Édition
• État de l'article: Neu
• Dimensions: 241 L x 160 H x 36 P (en millimètres) 241 millimètres de largeur par 160 millimètres de hauteur par 36 millimètres de profondeur
• Poids: 992 grammes

A propos de ce titre

Synopsis

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 because unsupervised methods are easy to learn since tensor decomposition is a conventional linear methodology. This book starts from very basic linear algebra and reaches the cutting edge methodologies applied to difficult situations when there are many features (variables) while only small number of samples are available. The author includes advanced descriptions about tensor decomposition including Tucker decomposition using high order singular value decomposition as well as higher order orthogonal iteration, and train tensor decomposition. The author concludes by showing unsupervised methods and their application to a wide range of topics.

À propos de l?auteur

Prof. Taguchi is currently a Professor at Department of Physics, Chuo University. Prof. Taguchi received a master degree in Statistical Physics from Tokyo Institute of Technology, Japan in 1986, and PhD degree in Non-linear Physics from Tokyo Institute of Technology, Tokyo, Japan in 1988. He worked at Tokyo Institute of Technology and Chuo University. He is with Chuo University (Tokyo, Japan) since 1997. He currently holds the Professor position at this university. His main research interests are in the area of Bioinformatics, especially, multi-omics data analysis using linear algebra. Dr. Taguchi has published a book on bioinformatics, more than 150 journal papers, book chapters and papers in conference proceedings and was recognized as top 2% scientist of the world in 3rd consecutive years (2021, 2022, 2023) according to analysis of Stanford University, USA and report of Elsevier in bioinformatics.