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001 978-3-031-60982-4
003 DE-He213
005 20251030112927.0
007 cr nn 008mamaa
008 240901s2024 sz | s |||| 0|eng d
020 _a9783031609824
_9978-3-031-60982-4
024 7 _a10.1007/978-3-031-60982-4
_2doi
050 4 _aTK5101-5105.9
072 7 _aTJK
_2bicssc
072 7 _aTEC041000
_2bisacsh
072 7 _aTJK
_2thema
100 1 _aTaguchi, Y-h.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_983407
245 1 0 _aUnsupervised Feature Extraction Applied to Bioinformatics
_h[electronic resource] :
_bA PCA Based and TD Based Approach /
_cby Y-h. Taguchi.
250 _a2nd ed. 2024.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2024.
300 _aXXII, 533 p. 243 illus., 211 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aUnsupervised and Semi-Supervised Learning,
_x2522-8498
505 0 _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.
520 _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.
650 0 _aTelecommunication.
650 0 _aBioinformatics.
_964050
650 0 _aSignal processing.
650 0 _aPattern recognition systems.
_965547
650 0 _aData mining.
_96941
650 1 4 _aCommunications Engineering, Networks.
650 2 4 _aComputational and Systems Biology.
_965974
650 2 4 _aSignal, Speech and Image Processing.
650 2 4 _aBioinformatics.
_964050
650 2 4 _aAutomated Pattern Recognition.
_965551
650 2 4 _aData Mining and Knowledge Discovery.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031609817
776 0 8 _iPrinted edition:
_z9783031609831
776 0 8 _iPrinted edition:
_z9783031609848
830 0 _aUnsupervised and Semi-Supervised Learning,
_x2522-8498
_968420
856 4 0 _uhttps://doi.org/10.1007/978-3-031-60982-4
912 _aZDB-2-ENG
912 _aZDB-2-SXE
999 _c62960
_d62960