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El. knyga: Data Fusion Methodology and Applications

Edited by (Associate Professor, University of Modena and Reggio Emilia, Modena, Italy)
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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.

  • 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
Contributors ix
Preface xi
1 Introduction: Ways and Means to Deal With Data From Multiple Sources
Marina Cocghi
1 Motivation
1(1)
2 Context, Definition
2(4)
3 Main Approaches
6(11)
4 Remarks in the User's Perspective
17(5)
References
22(5)
2 A Framework for Low-Level Data Fusion
Age K. Smilde
Iven Van Mechelen
1 Introduction and Motivation
27(4)
2 Data Structures
31(1)
3 Framework for Low-Level Data Fusion
32(6)
4 Common and Distinct Components
38(3)
5 Examples
41(6)
6 Conclusions
47(1)
References
47(4)
3 General Framing of Low-, Mid-, and High-Level Data Fusion With Examples in the Life Sciences
Agnieszka Smolinska
Jasper Engel
Ewa Szymanska
Lutgarde Buydens
Lionel Blanchet
1 Introduction
51(3)
2 Data Sampling, Measurements, and Preprocessing
54(1)
3 Data Fusion Strategy
55(10)
4 Data Fusion Strategies with Examples
65(7)
5 Interpretation of the Outcomes
72(3)
6 Conclusions
75(1)
References
76(5)
4 Numerical Optimization-Based Algorithms for Data Fusion
N. Vervliet
L. De Lathauwer
1 Introduction
81(4)
2 Numerical Optimization for Tensor Decompositions
85(6)
3 Canonical Polyadic Decomposition
91(9)
4 Constrained Decompositions
100(11)
5 Coupled Decompositions
111(6)
6 Large-Scale Computations
117(5)
References
122(7)
5 Recent Advances in High-Level Fusion Methods to Classify Multiple Analytical Chemical Data
D. Ballabio
R. Todeschini
V. Consonni
1 Introduction
129(3)
2 Methods
132(12)
3 Application on Analytical Data
144(4)
4 Results
148(4)
5 Conclusions
152(1)
References
153(4)
6 The Sequential and Orthogonalized PLS Regression for Multiblock Regression: Theory, Examples, and Extensions
Alessandra Biancolillo
Tormod Nas
1 Introduction
157(1)
2 How It Started
158(1)
3 Model and Algorithm
158(2)
4 Some Mathematical Formulae and Properties
160(1)
5 How to Choose the Optimal Number of Components
161(1)
6 How to Interpret the Models
162(1)
7 Some Further Properties of the SO-PLS Method
163(2)
8 Examples of Standard SO-PLS Regression
165(2)
9 Extensions and Modifications of SO-PLS
167(8)
10 Conclusions
175(1)
References
176(3)
7 ComDim Methods for the Analysis of Multiblock Data in a Data Fusion Perspective
V. Cariou
D. Jouan-Rimbaud Bouveresse
E.M. Qannari
D.N. Rutledge
1 Introduction
179(2)
2 Com Dim Analysis
181(4)
3 P-ComDim Analysis
185(4)
4 Path-ComDim Analysis
189(2)
5 Software
191(1)
6 Illustration
191(11)
7 Conclusion
202(1)
References
202(3)
8 Data Fusion by Multivariate Curve Resolution
Anna De Juan
R. Tauter
1 Introduction. General Multivariate Curve Resolution Framework. Why to Use It in Data Fusion?
205(3)
2 Data Fusion Structures in MCR. Multiset Analysis
208(3)
3 Constraints in MCR. Versatility Linked to Data Fusion. Hybrid Models (Hard---Soft, Bilinear/Multilinear)
211(7)
4 Limitations Overcome by Multiset MCR Analysis. Breaking Rank Deficiency and Decreasing Ambiguity
218(3)
5 Additional Outcomes of MCR Multiset Analysis. The Hidden Dimensions
221(1)
6 Data Fusion Fields
222(5)
7 Conclusions
227(1)
References
228(7)
9 Dealing With Data Heterogeneity in a Data Fusion Perspective: Models, Methodologies, and Algorithms
Federica Mandreoli
Manuela Montangero
1 Introduction
235(2)
2 Overview of Life Science Data Sources
237(2)
3 Addressing Data Heterogeneity
239(16)
4 Latest Trends and Challenges
255(9)
5 Conclusions
264(1)
References
265(6)
10 Data Fusion Strategies in Food Analysis
Alessandra Biancolillo
Ricard Boque
Marina Cocchi
Federico Marini
1 Introduction
271(2)
2 Chemometric Strategies Applied in Data Fusion
273(3)
3 Building, Optimization, and Validation of Data-Fused Models
276(2)
4 Applications
278(23)
5 Conclusions
301(1)
References
301(10)
11 Image Fusion
Anna De Juan
Aoife Gowen
Ludovic Duponchel
Cyril Ruckebusch
1 Introduction
311(3)
2 Image Fusion by Using Single Fused Data Structures
314(9)
3 Image Fusion by Connecting Different Images Through Regression Models
323(5)
4 Image Fusion for Superresolution Purposes
328(12)
5 Conclusions
340(1)
References
341(5)
12 Data Fusion of Nonoptimized Models: Applications to Outlier Detection, Classification, and Image Library Searching
John H. Kalivas
1 Outlier Detection
346(10)
2 Classification
356(8)
3 Thermal linage Analysis
364(4)
Acknowledgments
368(1)
References
368(3)
Index 371
Marina Cocchi currently serves as the Associate Professor in the University of Modena and Reggio Emilias 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.
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