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005 20240901130234.0
010   $a3-031-60982-4
024 7 $a10.1007/978-3-031-60982-4
035   $a(MiAaPQ)EBC31629555
035   $a(Au-PeEL)EBL31629555
035   $a(CKB)34674259000041
035   $a(DE-He213)978-3-031-60982-4
035   $a(EXLCZ)9934674259000041
100   $a20240901d2024      u|         0
101 0 $aeng
135   $aurcnu||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aUnsupervised Feature Extraction Applied to Bioinformatics $eA PCA Based and TD Based Approach /$fby Y-h. Taguchi
205   $a2nd ed. 2024.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2024.
215   $a1 online resource (542 pages)
225 1 $aUnsupervised and Semi-Supervised Learning,$x2522-8498
311   $a3-031-60981-6 
327   $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.
330   $aThis updated book proposes applications of tensor decomposition to unsupervised feature extraction and feature selection. The author posits that although supervised methods including deep learning have become popular, unsupervised methods have their own advantages. He argues that this is the case because unsupervised methods are easy to learn since tensor decomposition is a conventional linear methodology. This book starts from very basic linear algebra and reaches the cutting edge methodologies applied to difficult situations when there are many features (variables) while only small number of samples are available. The author includes advanced descriptions about tensor decomposition including Tucker decomposition using high order singular value decomposition as well as higher order orthogonal iteration, and train tensor decomposition. The author concludes by showing unsupervised methods and their application to a wide range of topics. Allows readers to analyze data sets with small samples and many features; Provides a fast algorithm, based upon linear algebra, to analyze big data; Includes several applications to multi-view data analyses, with a focus on bioinformatics.
410  0$aUnsupervised and Semi-Supervised Learning,$x2522-8498
606   $aTelecommunication
606   $aBioinformatics
606   $aSignal processing
606   $aPattern recognition systems
606   $aData mining
606   $aCommunications Engineering, Networks
606   $aComputational and Systems Biology
606   $aSignal, Speech and Image Processing
606   $aBioinformatics
606   $aAutomated Pattern Recognition
606   $aData Mining and Knowledge Discovery
615  0$aTelecommunication.
615  0$aBioinformatics.
615  0$aSignal processing.
615  0$aPattern recognition systems.
615  0$aData mining.
615 14$aCommunications Engineering, Networks.
615 24$aComputational and Systems Biology.
615 24$aSignal, Speech and Image Processing.
615 24$aBioinformatics.
615 24$aAutomated Pattern Recognition.
615 24$aData Mining and Knowledge Discovery.
676   $a621.382
700   $aTaguchi$b Y-h$01063074
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910886080603321
996   $aUnsupervised Feature Extraction Applied to Bioinformatics$94252782
997   $aUNINA