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Unsupervised Feature Extraction Applied to Bioinformatics, 2 Ed.
出版済み 3週間でお届けいたします。
A PCA Based and TD Based Approach Series: Unsupervised and Semi-Supervised Learning Author: Taguchi Publisher: Springer ISBN: 9783031609817 Cover: HARDCOVER Date: 2024年09月 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. TABLE OF CONTENTS Mathematical preparations Introduction to Linear Algebra Matrix Factorization Tensor Decomposition Feature extractions PCA-Based Unsupervised FE TD-Based Unsupervised FE Applications to Bioinformatics Applications of PCA-Based Unsupervised FE to Bioinformatics Application of TD-Based Unsupervised FE to Bioinformatics Theoretical Investigation of TD- and PCA-Based Unsupervised FE
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