Principal component analysis of symmetric fuzzy data
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Publication:956852
DOI10.1016/S0167-9473(02)00352-3zbMath1429.62338OpenAlexW2158822003MaRDI QIDQ956852
Henk A. L. Kiers, Paolo E. Giordani
Publication date: 26 November 2008
Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/s0167-9473(02)00352-3
Factor analysis and principal components; correspondence analysis (62H25) Fuzziness, and linear inference and regression (62J86)
Related Items (13)
Principal Component Analysis for Non-Precise Data ⋮ Three-way analysis of imprecise data ⋮ Two- and three-way component models for lr fuzzy data in a possibilistic framework ⋮ Clustering reduced interval data using Hausdorff distance ⋮ Principal components of sample estimates: an approach through symbolic data analysis ⋮ On possibilistic clustering with repulsion constraints for imprecise data ⋮ A weighted fuzzy \(C\)-means clustering model for fuzzy data ⋮ Principal component histograms from interval-valued observations ⋮ Component models for fuzzy data ⋮ A possibilistic approach to latent component analysis for symmetric fuzzy data ⋮ A comparison of three methods for principal component analysis of fuzzy interval data ⋮ Principal component analysis for interval‐valued observations ⋮ Fuzzy measures for a correlation coefficient of fuzzy numbers under \(T_{\text W}\)(the weakest t-norm)-based fuzzy arithmetic operations
Cites Work
- Fuzzy least squares
- Linear regression analysis for fuzzy/crisp input and fuzzy/crisp output data
- Fuzzy sets and systems. Theory and applications
- On a class of fuzzy \(c\)-numbers clustering procedures for fuzzy data
- A least-squares approach to fuzzy linear regression analysis.
- Hierarchical relations between methods for simultaneous component analysis and a technique for rotation to a simple simultaneous structure
- Analysis of symbolic data. Exploratory methods for extracting statistical information from complex data
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