Data Fusion Methodology and Applications 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.
- 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
Sprache
Verlagsort
Verlagsgruppe
Elsevier Science & Techn.
ISBN-13
978-0-444-63985-1 (9780444639851)
Schweitzer Klassifikation
1. Introduction: ways and means to deal with data from multiple sources2. Framework for low-level data fusion3. General framing of low-high-mid level Data Fusion with examples in life science4. Numerical optimization based algorithms for data fusion5. Recent advances in High-Level Fusion Methods to classify multiple analytical Chemical Data6. SO-(N)-PLS: Sequentially Orthogonalized-(N)-PLS in Data Fusion context7. ComDim methods for the analysis of multi block data in a data fusion perspective8. Data fusion via multiset analysis 9. Dealing with data heterogeneity in a data fusion perspecitve: models, methodologies, and algorithms10. Data Fusion strategies in food analysis11. Data fusion for image analysis12. Data fusion using window based models: Application to outlier detection, classification, and forensic image analysis
