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Unsupervised Feature Extraction Applied to Bioinformatics: A PCA Based and TD Based Approach (Unsupervised and Semi-Supervised Learning) - Tapa dura
Sinopsis
This 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.
"Sinopsis" puede pertenecer a otra edición de este libro.
Acerca del autor
Prof. Taguchi is currently a Professor at Department of Physics, Chuo University. Prof. Taguchi received a master degree in Statistical Physics from Tokyo Institute of Technology, Japan in 1986, and PhD degree in Non-linear Physics from Tokyo Institute of Technology, Tokyo, Japan in 1988. He worked at Tokyo Institute of Technology and Chuo University. He is with Chuo University (Tokyo, Japan) since 1997. He currently holds the Professor position at this university. His main research interests are in the area of Bioinformatics, especially, multi-omics data analysis using linear algebra. Dr. Taguchi has published a book on bioinformatics, more than 100 journal papers, book chapters and papers in conference proceedings.
De la contraportada
This 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 analyzedata 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.
"Sobre este título" puede pertenecer a otra edición de este libro.
- EditorialSpringer
- Año de publicación2019
- ISBN 10 3030224554
- ISBN 13 9783030224554
- EncuadernaciónTapa dura
- IdiomaInglés
- Número de páginas340
- Contacto del fabricanteno disponible
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Unsupervised Feature Extraction Applied to Bioinformatics: A PCA Based and TD Based Approach (Unsupervised and Semi-Supervised Learning) [Hardcover] Taguchi, Y-h.
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Springer, 2019
ISBN 10: 3030224554
ISBN 13: 9783030224554
Antiguo o usado
Tapa dura
Original o primera edición
Librería: SpringBooks, Berlin, Alemania
Calificación del vendedor: 5 de 5 estrellas
Hardcover. Condición: Very Good. 1. Auflage. Unread, with some shelfwear. Immediately dispatched from Germany. Nº de ref. del artículo: CEE-2402C-BIKINI-10-1000
Cantidad disponible: 1 disponibles
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Unsupervised Feature Extraction Applied to Bioinformatics: A PCA Based and TD Based Approach
Librería: Revaluation Books, Exeter, Reino Unido
Calificación del vendedor: 5 de 5 estrellas
Hardcover. Condición: Brand New. 321 pages. 9.25x6.25x0.75 inches. In Stock. Nº de ref. del artículo: zk3030224554
Cantidad disponible: 1 disponibles