Advances in principal balances for compositional data
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Publication:1719875
DOI10.1007/s11004-017-9712-zzbMath1407.62219OpenAlexW2769486068MaRDI QIDQ1719875
R. Tolosona-Delgado, Juan José Egozcue, Vera Pawlowsky-Glahn, Josep Antoni Martín-Fernández
Publication date: 12 February 2019
Published in: Mathematical Geosciences (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s11004-017-9712-z
compositionsprincipal component analysiscluster analysissimplexAitchison normisometric logratio coordinates
Directional data; spatial statistics (62H11) Factor analysis and principal components; correspondence analysis (62H25) Geostatistics (86A32)
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Uses Software
Cites Work
- Dealing with distances and transformations for fuzzy \(c\)-means clustering of compositional data
- Isometric logratio transformations for compositional data analysis
- Groups of parts and their balances in compositional data analysis
- Simulation of random dendrograms and comparison tests: Some comments
- Principal component analysis.
- Cluster Analysis
- Biplots of Compositional Data
- Modern Multivariate Statistical Techniques
- Interpretable dimension reduction
- Principal component analysis of compositional data
- Sparse principal balances
- A clustering approach to interpretable principal components
- Simple components
- Geometric approach to statistical analysis on the simplex
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- Unnamed Item
- Unnamed Item
