Factor analysis latent subspace modeling and robust fuzzy clustering using t-distributions
IEEE Transactions on Fuzzy Systems
PCA-Guided k-Means with Variable Weighting and Its Application to Document Clustering
MDAI '09 Proceedings of the 6th International Conference on Modeling Decisions for Artificial Intelligence
Fuzzy PCA-guided robust k-means clustering
IEEE Transactions on Fuzzy Systems
A family of fuzzy learning algorithms for robust principal component analysis neural networks
IEEE Transactions on Fuzzy Systems
Local subspace learning by extended fuzzy c-medoids clustering
International Journal of Knowledge Engineering and Soft Data Paradigms
A method for training finite mixture models under a fuzzy clustering principle
Fuzzy Sets and Systems
Expert Systems with Applications: An International Journal
Pattern Recognition
Several formulations for graded possibilistic approach to fuzzy clustering
RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
A new approach to fuzzification of memberships in cluster analysis
MDAI'05 Proceedings of the Second international conference on Modeling Decisions for Artificial Intelligence
Alternative fuzzy c-lines and local principal component extraction
International Journal of Knowledge Engineering and Soft Data Paradigms
A comparative study on TIBA imputation methods in FCMdd-based linear clustering with relational data
Advances in Fuzzy Systems - Special issue on Fuzzy Functions, Relations, and Fuzzy Transforms: Theoretical Aspects and Applications to Fuzzy Systems
Kernelized fuzzy c-means method and gaussian mixture model in unsupervised cascade clustering
ITIB'12 Proceedings of the Third international conference on Information Technologies in Biomedicine
Fuzzy Cluster Validation Based on Fuzzy PCA-Guided Procedure
International Journal of Fuzzy System Applications
Expert Systems with Applications: An International Journal
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Fuzzy$c$-means (FCM)-type fuzzy clustering approaches are closely related to Gaussian mixture models (GMMs) and EM-like algorithms have been used in FCM clustering with regularized objective functions. Especially, FCM with regularization by Kullback–Leibler information (KLFCM) is a fuzzy counterpart of GMMs. In this paper, we propose to apply probabilistic principal component analysis (PCA) mixture models to linear clustering following a discussion on the relationship between local PCA and linear fuzzy clustering. Although the proposed method is a kind of the constrained model of KLFCM, the algorithm includes the fuzzy$c$-varieties (FCV) algorithm as a special case, and the algorithm can be regarded as a modified FCV algorithm with regularization by K–L information. Numerical experiments demonstrate that the proposed clustering algorithm is more flexible than the maximum likelihood approaches and is useful for capturing local substructures properly.