Springer - Unsupervised Feature Extraction in Bioinformatics

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.