Data Fusion Methodology and Applications:
Gespeichert in:
| Weitere Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Amsterdam
Elsevier
2019
|
| Schriftenreihe: | Data handling in science and technology
Volume 31 |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | xii, 383 Seiten Illustrationen, Diagramme |
| ISBN: | 9780444639844 |
Internformat
MARC
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Datensatz im Suchindex
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Contents Contributors ix Preface X1 1. Introduction: Ways and Means to Deal With Data From Multiple Sources MARINA COCCHI 1. Motivation 2. Context, Definition 3. Main Approaches 4. Remarks in the User’s Perspective References 1 2 6 17 22 2. A Framework for Low-Level Data Fusion AGE K. SMILDE AND IVEN VAN MECHELEN 1. Introduction and Motivation 2. Data Structures 3. Framework for Low-Level Data Fusion 4. Common and Distinct Components 5. Examples 6. Conclusions References 27 31 32 38 41 47 47 3. General Framing of Low-, Mid-, and High-Level Data Fusion With Examples in the Life Sciences AGNIESZKA SMOLIŃSKA, JASPER ENGEL, EWA SZYMAŃSKA, LUTGARDE BUYDENS, AND LIONEL BLANCHET 1. Introduction 2. Data Sampling, Measurements, and Preprocessing 3. Data Fusion Strategy 4. Data Fusion Strategies with Examples 5. Interpretation of the Outcomes 6. Conclusions References 51 54 55 65 72 75 76
VX CONTENTS 4 Numerical Optimization-Based Algorithms for Data Fusion N. VERVLIET AND L. DE LATHAUWER 1. Introduction 2 Numerical Optimization for Tensor Decompositions 3. Canonical Polyadic Decomposition 4. Constrained Decompositions 5. Coupled Decompositions 6. Large-Scale Computations References 81 85 91 100 111 117 122 5. Recent Advances in High-Level Fusion Methods to Classify Multiple Analytical Chemical Data D. BALLABIO, R. TODESCHINI, AND V. CONSONNi 1. Introduction 2. Methods 3. Application on Analytical Data 4. Results 5. Conclusions References 129 132 144 148 152 153 6. The Sequential and Orthogonalized PLS Regression for Multiblock Regression: Theory, Examples, and Extensions ALESSANDRA BIANCOLILLO AND TORMOD NÆS 1. Introduction 2. How It Started 3. Model and Algorithm 4. Some Mathematical Formulae and Properties 5. How to Choose the Optimal Number of Components 6. How to Interpret the Models 7. Some Further Properties of the SO-PLS Method 8. Examples of Standard SO-PLS Regression 9. Extensions and Modifications of SO-PLS 10. Conclusions References 157 158 158 160 161 162 163 165 167 175 176 7. ComDim Methods for the Analysis of Multiblock Data in a Data Fusion Perspective V. CARIOU, D. JOUAN-RIMBAUD BOUVERESSE, E.M. QANNARI, AND D.N. RUTLEDGE 1. 2. 3. 4· 5. Introduction ComDim Analysis Р-ComDim Analysis Path-ComDim Analysis Software 179 181 185 189 191
CONTENTS 6. Illustration 7. Conclusion References Vii 191 202 202 8. Data Fusion by Multivariate Curve Resolution ANNA DE JUAN AND R. TAULER 1 Introduction. General Multivariate Curve Resolution Framework. Why to Use Itin DataFusion? 2. Data FusionStructures inMCR. MultisetAnalysis 3. Constraints in MCR. Versatility Linked to Data Fusion. Hybrid Models (Hard—Soft, Bilinear/Multilinear) 205 208 211 4. Limitations Overcome by Multiset MCR Analysis, Breaking Rank Deficiency and Decreasing Ambiguity 5. Additional Outcomes of MCR Multiset Analysis. The Hidden Dimensions 6. Data Fusion Fields 7. Conclusions References 218 221 222 227 228 9. Dealing With Data Heterogeneity in a Data Fusion Perspective: Models, Methodologies, and Algorithms FEDERICA MANDREOLI AND MANUELA MONTANGERO 1. Introduction 2. Overview of Life Science Data Sources 3. Addressing Data Heterogeneity 4. Latest Trends and Challenges 5. Conclusions References 235 237 239 255 264 265 10. Data Fusion Strategies in Food Analysis ALESSANDRA BIANCOL1LLO, RICARD BOQUE, MARINA COCCHI, AND FEDERICO MARINI 1. Introduction 2. Chemometric Strategies Applied in Data Fusion 3. Building, Optimization, and Validation of Data-Fused Models 4- Applications 5. Conclusions References 271 273 276 278 301 301 11. Image Fusion ANNA DE JUAN, AOIFE GOWEN, LUDOVIC DUPONCHEL, AND CYRIL RUCKEBUSCH 1. Introduction 2. Image Fusion by Using Single FusedData Structures 3. Image Fusion by Connecting Different ImagesThrough Regression Models 311 314 323
viii CONTENTS 4. Image Fusion for Superresolution Purposes 5. Conclusions References 32g 340 34լ 12. Data Fusion of Nonoptimized Models: Applications to Outlier Detection, Classińcation, and Image Library Searching JOHN H. KALIVAS 1. Outlier Detection 2. Classification 3. Thermal Image Analysis Acknowledgments References Index 346 356 364 368 368 371 |
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| indexdate | 2024-12-08T23:00:19Z |
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| isbn | 9780444639844 |
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| spelling | Data Fusion Methodology and Applications edited by Marina Cocchi Amsterdam Elsevier 2019 xii, 383 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Data handling in science and technology Volume 31 Datenfusion (DE-588)4582612-2 gnd rswk-swf Datenfusion (DE-588)4582612-2 s DE-604 Cocchi, Marina 1961- (DE-588)1189764571 edt Data handling in science and technology Volume 31 (DE-604)BV002802448 31 Digitalisierung UB Augsburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=031408250&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
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| subject_GND | (DE-588)4582612-2 |
| title | Data Fusion Methodology and Applications |
| title_auth | Data Fusion Methodology and Applications |
| title_exact_search | Data Fusion Methodology and Applications |
| title_full | Data Fusion Methodology and Applications edited by Marina Cocchi |
| title_fullStr | Data Fusion Methodology and Applications edited by Marina Cocchi |
| title_full_unstemmed | Data Fusion Methodology and Applications edited by Marina Cocchi |
| title_short | Data Fusion Methodology and Applications |
| title_sort | data fusion methodology and applications |
| topic | Datenfusion (DE-588)4582612-2 gnd |
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