Data Fusion Methodology and Applications

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

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• Química analítica

Algorithms; Analytical technique; Bayesian rules; Beer; Canonical polyadic decomposition; Chemometrics; Classification; ComDim; Consensus modeling; Constrained decomposition; Coupled decomposition; Data fusion; Data heterogeneity; Data integration; Dempster-Shafer; Ensemble methods; Entity resolution; Food quality; Functional genomics; Gas chromatography-mass spectrometry; Gauss-Newton; High-level; Hybrid hard and soft modeling; Image fusion; Image regression; Incomplete multisets; Information fusion; Kernel-based data fusion; Life science data sources; Liquid chromatography; Lock-in thermography; Majority voting; MCR-ALS; Metabolomics; Microbiome data; Model constraints; Multi-block analysis; Multiblock data analysis; Multiblock regression; Multiblock; Multimodal image; Multimodality; Multiset analysis; Multiset data; Multiset; Multivariate curve resolution-alternating least squares (MCR-ALS)Multiway; Numerical optimization; Olive oil; Outlier detection; Path-ComDim; P-ComDim; SO-N-PLS; SO-N-PLS-LDA;