Read Unsupervised Feature Extraction Applied to Bioinformatics

Unsupervised Feature Extraction Applied to Bioinformatics

Y-h. Taguchi

4.9/5 (23253 ratings)

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.We have made it easy for you to find a PDF Ebooks without any digging. And by having access to our ebooks online or by storing it on your computer, you have convenient answers with Unsupervised Feature Extraction Applied to Bioinformatics. To get started finding Unsupervised Feature Extraction Applied to Bioinformatics, you are right to find our website which has a comprehensive collection of manuals listed.
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Pages: 542

Format: PDF, EPUB & Kindle Edition

Publisher: Springer Nature

Release: 2024

ISBN: adBp1aI2LWAC

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.We have made it easy for you to find a PDF Ebooks without any digging. And by having access to our ebooks online or by storing it on your computer, you have convenient answers with Unsupervised Feature Extraction Applied to Bioinformatics. To get started finding Unsupervised Feature Extraction Applied to Bioinformatics, you are right to find our website which has a comprehensive collection of manuals listed.
Our library is the biggest of these that have literally hundreds of thousands of different products represented.

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Unsupervised Feature Extraction Applied to Bioinformatics

Unsupervised Feature Extraction Applied to Bioinformatics

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