Multiblock Data Fusion in Statistics and Machine L earning: Applications in the Natural and Life Scie nces

This book provides an overview of multiblock data fusion methods in statistics and machine learning, with applications in the natural and life sciences. It covers methods based on components and latent variables, including practical examples and an R-package. It is ideal for graduate students and researchers in bioinformatics, sensometrics, and chemometrics.

Format: Hardback
Length: 416 pages
Publication date: 28 April 2022
Publisher: John Wiley and Sons Ltd

Multiblock data fusion is a crucial technique in statistics and machine learning, particularly in the natural and life sciences. It involves the integration of multiple data sets, often from different sources and domains, to obtain a more comprehensive understanding of the underlying phenomena. This expert guide explores the advantages and shortcomings of various forms of multiblock analysis, as well as the relationships between them.

Arising from fusion problems that exist in various fields, such as bioinformatics, sensometrics, and chemometrics, the methods available to fuse multiple data sets have expanded significantly in recent years. While older methods rooted in psychometrics and chemometrics still play a role, newer approaches based on components and latent variables have gained prominence.

This comprehensive book provides a detailed overview of all relevant multiblock data analysis methods for fusing multiple data sets. It focuses on methods based on components and latent variables, including both well-known and lesser-known methods with potential applications in different types of problems.

The book is organized into three main sections. The first section introduces the concept of multiblock data fusion and discusses the theoretical foundations and motivations behind it. It also provides an overview of the different options available for the fusion of multiple data sets, including methods originating in psychometrics and chemometrics.

The second section provides practical discussions of well-known and lesser-known methods with applications in a wide variety of data problems. Each method is illustrated by practical examples, and the accompanying R-code allows readers to apply the discussed methods directly to their own data.

The third section focuses on the application of multiblock data fusion in the natural and life sciences. It includes case studies and examples from bioinformatics, sensometrics, and chemometrics, demonstrating the usefulness of the discussed methods in real-world scenarios.

The distinguished authors of this book have created an accessible and useful guide to help readers fuse data, develop new data fusion models, understand the underlying algorithms and models, and appreciate the advantages and shortcomings of various approaches. Whether you are a graduate student studying data analysis in the context of the natural and life sciences or a professional seeking to enhance your understanding of multiblock data fusion, this book is an invaluable resource.

Weight: 1028g
Dimension: 183 x 261 x 28 (mm)
ISBN-13: 9781119600961