Unsupervised Feature Extraction Applied to Bioinformatics: A PCA Based and TD Based Approach (Second Edition 2024) (Unsupervised and Semi-Supervised Learning)

By: Y-h. Taguchi

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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.

Book Quality:: Publisher Quality
ISBN-13:: 9783031609824
Related ISBNs:: 9783031609817
Publisher:: Springer International Publishing
Date of Addition:: 10/05/24
Copyrighted By:: The Editor
Adult content:: No
Language:: English
Has Image Descriptions:: No
Categories:: Nonfiction, Science, Computers and Internet, Technology, Mathematics and Statistics, Medicine, Communication
Submitted By:: Bookshare Staff
Usage Restrictions:: This is a copyrighted book.