Expert Systems with Applications: An International Journal
Classification based on fuzzy robust PCA algorithms and similarity classifier
Expert Systems with Applications: An International Journal
A weighted fuzzy c-means clustering model for fuzzy data
Computational Statistics & Data Analysis
Fuzzy multidimensional scaling
Computational Statistics & Data Analysis
A comparison of three methods for principal component analysis of fuzzy interval data
Computational Statistics & Data Analysis
Three-way analysis of imprecise data
Journal of Multivariate Analysis
Fuzzy Sets and Systems
A family of fuzzy learning algorithms for robust principal component analysis neural networks
IEEE Transactions on Fuzzy Systems
A robust clustering procedure for fuzzy data
Computers & Mathematics with Applications
Maximum likelihood estimation from fuzzy data using the EM algorithm
Fuzzy Sets and Systems
K-sample tests for equality of variances of random fuzzy sets
Computational Statistics & Data Analysis
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This paper describes an extension of principal component analysis (PCA) allowing the extraction of a limited number of relevant features from high-dimensional fuzzy data. Our approach exploits the ability of linear autoassociative neural networks to perform information compression in just the same way as PCA, without explicit matrix diagonalization. Fuzzy input values are propagated through the network using fuzzy arithmetics, and the weights are adjusted to minimize a suitable error criterion, the inputs being taken as target outputs. The concept of correlation coefficient is extended to fuzzy numbers, allowing the interpretation of the new features in terms of the original variables. Experiments with artificial and real sensory evaluation data demonstrate the ability of our method to provide concise representations of complex fuzzy data.