Data Fusion Methodology and Applications - 1st Edition - ISBN: 9780444639844

Data Fusion Methodology and Applications, Volume 31

1st Edition

Editors: Marina Cocchi
Paperback ISBN: 9780444639844
Imprint: Elsevier
Published Date: 1st May 2019
Page Count: 400
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Table of Contents

1. Preface
2. Introduction
3. Framework for low-level data fusion
4. Numerical optimization based algorithms for data fusion
5. General framing of low-high-mid level Data Fusion with examples in life science
6. SO-(N)-PLS: Sequentially Orthogonalized-(N)-PLS in Data Fusion context
7. ComDim methods for the analysis of multi block data in a data fusion perspective
8. Data fusion via multiset analysis
9. Recent advances in High-Level Fusion Methods to classify multiple analytical Chemical Data
10. Data Fusion strategies in food analysis
11. Data fusion from a data management perspective: models, methodologies, and algorithms
12. Conceptual discussion of fusion: benefits, drawbacks, uniqueness, robustness, new possibilities for analysis
13. Data fusion for image analysis
14. Data fusion in process monitoring context
15. GSVD based approaches to combine genomic data in biomedicine
16. Regularized Generalized Canonical Correlation Analysis in the analysis of multimodal data with application to medical imaging


Description

The Handbook of Metabolic Phenotyping, Volume 33, explores the data-driven discovery paradigm in science and the need to handle large amounts of diverse data. Drivers of this change include the increased availability and accessibility of hyphenated analytical platforms, imaging techniques, the explosion of omics data, and the development of information technology. As data-driven research deals with an inductive attitude that aims to extract information and build models capable of inferring the underlying phenomena from the data itself, this book explores the challenges and methodologies used to integrate data from multiple sources, analytical platforms, different modalities, and varying timescales.

Key Features

  • Presents the first comprehensive textbook on data fusion, focusing on all aspects of data-driven discovery
  • Includes comprehensible, theoretical chapters written for large and diverse audiences
  • Provides a wealth of selected application to the topics included

Readership

Graduate students and researchers in chemical, biochemical, and biomedical disciplines where multi-analytical platforms are most diffuse/used (hyphenated instruments, imaging spectroscopies, microarrays, sensors, bio-sensors, etc.) and whose research areas include life science (systems biology, genomics, proteomics, metabolomics), food science (authentication, adulteration, sensory analysis, nutraceuticals), and industrial process monitoring


Details

No. of pages:
400
Language:
English
Copyright:
© Elsevier 2019
Published:
Imprint:
Elsevier
Paperback ISBN:
9780444639844

Ratings and Reviews


About the Editors

Marina Cocchi Editor

Marina Cocchi currently serves as the Associate Professor in the University of Modena and Reggio Emilia’s Department of Chemical and Geological Sciences. She has dedicated nearly two decades of chemometric and data analysis research to the university, exploring topics ranging from data fusion procedures to development and application of multivariates. Cocchi has also contributed to over one hundred scientific publications throughout her career.

Affiliations and Expertise

Associate Professor, University of Modena and Reggio Emilia, Modena, Italy