03421nam a22005175i 4500001001800000003000900018005001700027007001500044008004100059020001800100024003500118050001800153072001600171072002300187072001500210100007500225245014200300250001800442264007500460300006800535336002600603337002600629338003600655347002400691490005800715505033000773520119401103650002302297650002002320650002302340650003302363650001702396650004202413650003902455650004102494650002002535650003502555650004102590710003402631773002602665776003602691776003602727776003602763830005802799856004602857978-3-031-60982-4DE-He21320251030112927.0cr nn 008mamaa240901s2024    sz |    s    |||| 0|eng d  a97830316098247 a10.1007/978-3-031-60982-42doi 4aTK5101-5105.9 7aTJK2bicssc 7aTEC0410002bisacsh 7aTJK2thema1 aTaguchi, Y-h.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut10aUnsupervised Feature Extraction Applied to Bioinformaticsh[electronic resource] :bA PCA Based and TD Based Approach /cby Y-h. Taguchi.  a2nd ed. 2024. 1aCham :bSpringer International Publishing :bImprint: Springer,c2024.  aXXII, 533 p. 243 illus., 211 illus. in color.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda1 aUnsupervised and Semi-Supervised Learning,x2522-84980 aIntroduction 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.  aThis 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. 0aTelecommunication. 0aBioinformatics. 0aSignal processing. 0aPattern recognition systems. 0aData mining.14aCommunications Engineering, Networks.24aComputational and Systems Biology.24aSignal, Speech and Image Processing.24aBioinformatics.24aAutomated Pattern Recognition.24aData Mining and Knowledge Discovery.2 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978303160981708iPrinted edition:z978303160983108iPrinted edition:z9783031609848 0aUnsupervised and Semi-Supervised Learning,x2522-849840uhttps://doi.org/10.1007/978-3-031-60982-4