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Unsupervised Feature Extraction Applied to Bioinformatics

By Y-h. Taguchi

Unsupervised Feature Extraction Applied to Bioinformatics

By Y-h. Taguchi

0 - Default Title
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 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.
Product details
Binding:
Paperback
Edition:
2
Number of Pages:
556
Release Date:
2025-09-02
Publication Date:
2025-09-02
Publisher:
Springer
Languages:
Original: English
ISBN10:
3031609840
ISBN13:
9783031609848
GPSR Manufacturer Reference:
[email protected]
Weight:
832 g
Height:
155 cm
Width:
235 cm
Thickness:
30 cm
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