TY  - BOOK
AU  - Taguchi,Y-h.
ED  - SpringerLink (Online service)
TI  - Unsupervised Feature Extraction Applied to Bioinformatics: A PCA Based and TD Based Approach 
T2  - Unsupervised and Semi-Supervised Learning,
SN  - 9783031609824
AV  - TK5101-5105.9 
U1  - 621.382 23
PY  - 2024///
CY  - Cham
PB  - Springer International Publishing, Imprint: Springer
KW  - Telecommunication
KW  - Bioinformatics
KW  - Signal processing
KW  - Pattern recognition systems
KW  - Data mining
KW  - Communications Engineering, Networks
KW  - Computational and Systems Biology
KW  - Signal, Speech and Image Processing
KW  - Automated Pattern Recognition
KW  - Data Mining and Knowledge Discovery
N1  - 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
N2  - 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
UR  - http://libcon.rec.uabc.mx:2048/login?url=https://doi.org/10.1007/978-3-031-60982-4
ER  -