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
The adoption of a data-driven discovery paradigm in science has led to the need to handle large amounts of diverse data. Drivers of this change are on one hand the increased availability and accessibility of hyphenated analytical platforms, imaging techniques, and the explosion of omics data, and on the other hand the development of information technology. The Big Data issue is nowadays encompassing very different contexts and disciplines.
Data-driven research in general deals with an inductive attitude that aims at extracting information and building models capable of inferring the underlying phenomena from the data itself; in other words, data generated hypotheses are by and large replacing the generating data according to prior hypothesis deductive attitude.
Hence, the main challenge is how to face these multiple data sources and how to retrieve all possible available information. One of the salient aspects is the methodology to integrate data from multiple sources, analytical platforms, different modalities, and varying timescales, including also unstructured data. This is generally referred to as Data Fusion.
- This is the first comprehensive textbook on data fusion focusing on all the aspects in data-driven discovery
- Theoretical chapters are written in a way to be understandable to large and diverse audiences
- A wealth of selected application to hot topics are provided
Graduate students, researchers in chemical, biochemical, biomedical disciplines where multi-analytical platforms are most diffuse/used (hyphenated instruments, imaging spectroscopies, microarray, sensors, bio-sensors, etc.) and whose research areas include: life science (systems biology, genomics, proteomics, metabolomics), food science (authentication, adulteration, sensory analysis, nutraceuticals), industrial process monitoring
- No. of pages:
- © Elsevier 2019
- 1st September 2018
- Paperback ISBN:
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.
Associate Professor, University of Modena and Reggio Emilia, Modena, Italy