03763nam a22006015i 4500 978-3-031-60982-4 DE-He213 20251030112927.0 cr nn 008mamaa 240901s2024 sz | s |||| 0|eng d 9783031609824 978-3-031-60982-4 10.1007/978-3-031-60982-4 doi TK5101-5105.9 TJK bicssc TEC041000 bisacsh TJK thema Taguchi, Y-h. author. aut http://id.loc.gov/vocabulary/relators/aut 83407 Unsupervised Feature Extraction Applied to Bioinformatics [electronic resource] : A PCA Based and TD Based Approach / by Y-h. Taguchi. 2nd ed. 2024. Cham : Springer International Publishing : Imprint: Springer, 2024. XXII, 533 p. 243 illus., 211 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Unsupervised and Semi-Supervised Learning, 2522-8498 Introduction to linear algebra -- Matrix factorization -- Tensor decompositions -- PCA based unsupervised FE -- TD based unsupervised FE -- Application of PCA based unsupervised FE to bioinformatics -- Application of TD based unsupervised FE to bioinformatics -- Theoretical investigation of TD and PCA based unsupervised FE. 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. Allows readers to analyze data sets with small samples and many features; Provides a fast algorithm, based upon linear algebra, to analyze big data; Includes several applications to multi-view data analyses, with a focus on bioinformatics. Telecommunication. Bioinformatics. 64050 Signal processing. Pattern recognition systems. 65547 Data mining. 6941 Communications Engineering, Networks. Computational and Systems Biology. 65974 Signal, Speech and Image Processing. Bioinformatics. 64050 Automated Pattern Recognition. 65551 Data Mining and Knowledge Discovery. SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031609817 Printed edition: 9783031609831 Printed edition: 9783031609848 Unsupervised and Semi-Supervised Learning, 2522-8498 68420 https://doi.org/10.1007/978-3-031-60982-4 ZDB-2-ENG ZDB-2-SXE 62960 62960