£139.99

Springer - Unsupervised Feature Extraction in Bioinformatics

Name: Springer - Unsupervised Feature Extraction in Bioinformatics
Brand: Springer
Price: 139.99 GBP
Availability: InStock

Data Mining

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Description

Explore the power of unsupervised learning with this specialized text from Springer. While supervised methods like deep learning are widely used, this book highlights the specific advantages of unsupervised approaches. It focuses on the application of tensor decomposition for feature extraction and feature selection, offering a practical alternative to modern trends. Designed to be accessible, the content builds from fundamental linear algebra principles to advanced, cutting-edge methodologies. This progression allows readers to master complex techniques used to solve difficult data situations, particularly when dealing with high-dimensional datasets where many features exist alongside limited samples. Whether you are studying PCA-based or TD-based approaches, this book provides a clear path through the mathematical foundations and their real-world applications in bioinformatics.

Key Features

Explores tensor decomposition applications for effective unsupervised feature extraction and selection processes.

Provides a clear learning path starting from basic linear algebra and moving to advanced methodologies.

Offers a practical alternative to deep learning by utilizing conventional linear methodologies.

Addresses complex data challenges involving high feature counts and limited sample sizes.

Covers both PCA-based and TD-based approaches for bioinformatics research and data mining.

Product Specifications

Brand: Springer
Format: paperback
ASIN: 3030224589
Category: Books > Subjects > Computing & Internet > Databases > Data Storage & Management > Data Mining
Domain: Amazon UK
Release Date: 05 September 2020
Listed Since: 26 July 2020

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Springer - Unsupervised Feature Extraction in Bioinformatics

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