Unsupervised Feature Extraction Applied to Bioinformatics

A PCA Based and TD Based Approach

AvY-h. Taguchi

Inbunden, Engelska, 2024

Del i serien Unsupervised and Semi-Supervised Learning

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Beskrivning

This updated book proposes applications of tensor decomposition to unsupervised feature extraction and feature selection. 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.

Produktinformation

Utgivningsdatum:2024-09-01
Mått:155 x 235 x 36 mm
Vikt:990 g
Format:Inbunden
Språk:Engelska
Serie:Unsupervised and Semi-Supervised Learning
Antal sidor:533
Upplaga:2
Förlag:Springer International Publishing AG
ISBN:9783031609817

Utforska kategorier

Algebra inom Naturvetenskap och teknik
Artificiell intelligens inom Data och IT
Biovetenskap inom Naturvetenskap och teknik

Mer om författaren

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.

Innehållsförteckning

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.