Unsupervised Feature Extraction Applied to Bioinformatics:A PCA Based and TD Based Approach (Unsupervised and Semi-Supervised Leaing)

by: Y-h. Taguchi (Author)

Publisher: Springer

Edition: Second Edition 2024

Publication Date: 2024/9/1

Language: English

Print Length: 555 pages

ISBN-10: 3031609816

ISBN-13: 9783031609817

Book Description

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

About the Author

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

未经允许不得转载：Wow! eBook » Unsupervised Feature Extraction Applied to Bioinformatics:A PCA Based and TD Based Approach (Unsupervised and Semi-Supervised Leaing)