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portada Unsupervised Feature Extraction Applied to Bioinformatics. A PCA Based and TD Based Approach
Formato
Libro Físico
Colección
Unsupervised and Semi-Supervised Learning
Año
2025
N° páginas
533
Encuadernación
Tapa Blanda
Dimensiones
23.5x15.5 cm
ISBN13
9783031609848

Unsupervised Feature Extraction Applied to Bioinformatics. A PCA Based and TD Based Approach

Y-H. Taguchi (Autor) · Springer International Publishing AG · Tapa Blanda

Unsupervised Feature Extraction Applied to Bioinformatics. A PCA Based and TD Based Approach - Y-h. Taguchi

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Reseña del libro "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 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. 

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