Unsupervised Feature Extraction Applied to Bioinformatics A PCA Based and TD Based Approach /
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 be...
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| Format: | Elektroniczne E-book |
| Język: | angielski |
| Wydane: |
Cham :
Springer International Publishing : Imprint: Springer,
2024.
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| Wydanie: | 2nd ed. 2024. |
| Seria: | Unsupervised and Semi-Supervised Learning,
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| Hasła przedmiotowe: | |
| Dostęp online: | https://doi.org/10.1007/978-3-031-60982-4 |
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| 245 | 1 | 0 | |a Unsupervised Feature Extraction Applied to Bioinformatics |h [electronic resource] : |b A PCA Based and TD Based Approach / |c by Y-h. Taguchi. |
| 250 | |a 2nd ed. 2024. | ||
| 264 | 1 | |a Cham : |b Springer International Publishing : |b Imprint: Springer, |c 2024. | |
| 300 | |a XXII, 533 p. 243 illus., 211 illus. in color. |b online resource. | ||
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| 490 | 1 | |a Unsupervised and Semi-Supervised Learning, |x 2522-8498 | |
| 505 | 0 | |a 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. | |
| 520 | |a 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. | ||
| 532 | 8 | |a Accessibility summary: This PDF does not fully comply with PDF/UA standards, but does feature limited screen reader support, described non-text content (images, graphs), bookmarks for easy navigation and searchable, selectable text. Users of assistive technologies may experience difficulty navigating or interpreting content in this document. We recognize the importance of accessibility, and we welcome queries about accessibility for any of our products. If you have a question or an access need, please get in touch with us at accessibilitysupport@springernature.com. | |
| 532 | 8 | |a No reading system accessibility options actively disabled | |
| 532 | 8 | |a Publisher contact for further accessibility information: accessibilitysupport@springernature.com | |
| 650 | 0 | |a Telecommunication. | |
| 650 | 0 | |a Bioinformatics. | |
| 650 | 0 | |a Signal processing. | |
| 650 | 0 | |a Pattern recognition systems. | |
| 650 | 0 | |a Data mining. | |
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| 650 | 2 | 4 | |a Computational and Systems Biology. |
| 650 | 2 | 4 | |a Signal, Speech and Image Processing. |
| 650 | 2 | 4 | |a Bioinformatics. |
| 650 | 2 | 4 | |a Automated Pattern Recognition. |
| 650 | 2 | 4 | |a Data Mining and Knowledge Discovery. |
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| 776 | 0 | 8 | |i Printed edition: |z 9783031609817 |
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