Exploration of Data Fusion Strategies Using Principal Component Analysis and Multiple Factor Analysis Article Swipe
Mpho Mafata
,
Jeanne Brand
,
Martin Kidd
,
Andrei Medvedovici
,
Astrid Buica
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/beverages8040066
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/beverages8040066
In oenology, statistical analyses are used for descriptive purposes, mostly with separate sensory and chemistry data sets. Cases that combine them are mostly supervised, usually seeking to optimize discrimination, classification, or prediction power. Unsupervised methods are used as preliminary steps to achieving success in supervised models. However, there is potential for unsupervised methods to combine different data sets into comprehensive, information-rich models. This study detailed stepwise strategies for creating data fusion models using unsupervised techniques at different levels. Principal component analysis (PCA) and multiple factor analysis (MFA) were used to combine five data blocks (four chemistry and one sensory). The model efficiency and configurational similarity were evaluated using eigenvalues and regression vector (RV) coefficients, respectively. The MFA models were less efficient than PCA, having gradual distributions of eigenvalues across model dimensions. The MFA models were more representative than PCA, as indicated by high RV coefficients between MFA and each individual block. Therefore, MFA approaches were better suited for multi-modal data than PCA. This work approached data fusion systematically and showed the type of decisions that must be made and how to evaluate their consequences. Proper integration of data sets, instead of concatenation, is an important aspect to consider in multi-modal data fusion.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/beverages8040066
- https://www.mdpi.com/2306-5710/8/4/66/pdf?version=1667270183
- OA Status
- gold
- Cited By
- 10
- References
- 42
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4307327845
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4307327845Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/beverages8040066Digital Object Identifier
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Exploration of Data Fusion Strategies Using Principal Component Analysis and Multiple Factor AnalysisWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-10-21Full publication date if available
- Authors
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Mpho Mafata, Jeanne Brand, Martin Kidd, Andrei Medvedovici, Astrid BuicaList of authors in order
- Landing page
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https://doi.org/10.3390/beverages8040066Publisher landing page
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https://www.mdpi.com/2306-5710/8/4/66/pdf?version=1667270183Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2306-5710/8/4/66/pdf?version=1667270183Direct OA link when available
- Concepts
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Principal component analysis, Concatenation (mathematics), Computer science, Artificial intelligence, Sensor fusion, Pattern recognition (psychology), Data mining, Sparse PCA, Machine learning, Predictive modelling, Mathematics, CombinatoricsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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10Total citation count in OpenAlex
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2025: 2, 2024: 4, 2023: 4Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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