| 000 | 03699nam a22006015i 4500 | ||
|---|---|---|---|
| 001 | 978-3-030-22456-1 | ||
| 003 | DE-He213 | ||
| 005 | 20220801214001.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 190823s2020 sz | s |||| 0|eng d | ||
| 020 |
_a9783030224561 _9978-3-030-22456-1 |
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| 024 | 7 |
_a10.1007/978-3-030-22456-1 _2doi |
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| 050 | 4 | _aTK5101-5105.9 | |
| 072 | 7 |
_aTJK _2bicssc |
|
| 072 | 7 |
_aTEC041000 _2bisacsh |
|
| 072 | 7 |
_aTJK _2thema |
|
| 082 | 0 | 4 |
_a621.382 _223 |
| 100 | 1 |
_aTaguchi, Y-h. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _935608 |
|
| 245 | 1 | 0 |
_aUnsupervised Feature Extraction Applied to Bioinformatics _h[electronic resource] : _bA PCA Based and TD Based Approach / _cby Y-h. Taguchi. |
| 250 | _a1st ed. 2020. | ||
| 264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2020. |
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| 300 |
_aXVIII, 321 p. 111 illus., 94 illus. in color. _bonline resource. |
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| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bc _2rdamedia |
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| 338 |
_aonline resource _bcr _2rdacarrier |
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| 347 |
_atext file _bPDF _2rda |
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| 490 | 1 |
_aUnsupervised and Semi-Supervised Learning, _x2522-8498 |
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| 505 | 0 | _aIntroduction to linear algebra -- Matrix factorization -- Tensor decompositions -- PCA based unsupervised FE -- TD based unsupervised FE -- Application of PCA/TD based unsupervised FE to bioinformatics -- Application of TD based unsupervised FE to bioinformatics. | |
| 520 | _aThis 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 tenor 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. _910437 |
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| 650 | 0 |
_aBioinformatics. _99561 |
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| 650 | 0 |
_aSignal processing. _94052 |
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| 650 | 0 |
_aPattern recognition systems. _93953 |
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| 650 | 0 |
_aData mining. _93907 |
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| 650 | 1 | 4 |
_aCommunications Engineering, Networks. _931570 |
| 650 | 2 | 4 |
_aComputational and Systems Biology. _931619 |
| 650 | 2 | 4 |
_aSignal, Speech and Image Processing . _931566 |
| 650 | 2 | 4 |
_aBioinformatics. _99561 |
| 650 | 2 | 4 |
_aAutomated Pattern Recognition. _931568 |
| 650 | 2 | 4 |
_aData Mining and Knowledge Discovery. _935609 |
| 710 | 2 |
_aSpringerLink (Online service) _935610 |
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| 773 | 0 | _tSpringer Nature eBook | |
| 776 | 0 | 8 |
_iPrinted edition: _z9783030224554 |
| 776 | 0 | 8 |
_iPrinted edition: _z9783030224578 |
| 776 | 0 | 8 |
_iPrinted edition: _z9783030224585 |
| 830 | 0 |
_aUnsupervised and Semi-Supervised Learning, _x2522-8498 _935611 |
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| 856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-030-22456-1 |
| 912 | _aZDB-2-ENG | ||
| 912 | _aZDB-2-SXE | ||
| 942 | _cEBK | ||
| 999 |
_c75825 _d75825 |
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