Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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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

Hardcover

Series: Unsupervised and Semi-Supervised Learning

Technology & EngineeringBiologyGeneral Science

ISBN10: 3031609816
ISBN13: 9783031609817
Publisher: Springer
Published: Sep 1 2024
Pages: 533
Weight: 2.09
Height: 1.19 Width: 6.14 Depth: 9.21
Language: English

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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