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portada Unsupervised Feature Extraction Applied to Bioinformatics: A Pca Based and TD Based Approach
Type
Physical Book
Publisher
Author
Language
English
Pages
510
Format
Hardcover
ISBN13
9783031609817
Edition No.
0002
Categories

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

Taguchi, Y-H (Author) · Springer · Hardcover

Unsupervised Feature Extraction Applied to Bioinformatics: A Pca Based and TD Based Approach - Taguchi, Y-H

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Synopsis "Unsupervised Feature Extraction Applied to Bioinformatics: A Pca Based and TD Based Approach"

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.

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