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Elsevier

Data Fusion Methodology and Applications

  • 1 Edición, Volumen 31 - 11 de mayo de 2019
  • Última edición
  • Editor: Marina Cocchi
  • Idioma: Inglés

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 in… Leer más

Descripción

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.

Puntos claves

  • 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

De interès para

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

Índice

1. Introduction: ways and means to deal with data from multiple sources

2. Framework for low-level data fusion

3. General framing of low-high-mid level Data Fusion with examples in life science

4. Numerical optimization based algorithms for data fusion

5. Recent advances in High-Level Fusion Methods to classify multiple analytical Chemical Data

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. Dealing with data heterogeneity in a data fusion perspecitve: models, methodologies, and algorithms

10. Data Fusion strategies in food analysis

11. Data fusion for image analysis

12. Data fusion using window based models: Application to outlier detection, classification, and forensic image analysis

Detalles del producto

  • Edición: 1
  • Última edición
  • Volumen: 31
  • Publicado: 14 de mayo de 2019
  • Idioma: Inglés

Sobre el editor

MC

Marina Cocchi

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.
Afiliaciones y experiencia
Associate Professor, University of Modena and Reggio Emilia, Modena, Italy

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