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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Unsupervised Feature Extraction Applied to Bioinformatics: A Pca Based and TD Based Approach

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

Taguchi, Y-H

Paperback

Series: Unsupervised and Semi-Supervised Learning

Technology & EngineeringAnatomy & PhysiologyGeneral Computers

Currently unavailable to order

ISBN10: 3030224589
ISBN13: 9783030224585
Publisher: Springer
Published: Sep 5 2020
Pages: 321
Weight: 1.05
Height: 0.71 Width: 6.10 Depth: 9.25
Language: English

This 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 tenor decomposition. The author concludes by showing unsupervised methods and their application to a wide range of topics.

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