Facial expression recognition: a clustering-based approach
Pattern Recognition Letters
Generalized Discriminant Analysis Using a Kernel Approach
Neural Computation
Possibilistic approach to kernel-based fuzzy c-means clustering with entropy regularization
MDAI'05 Proceedings of the Second international conference on Modeling Decisions for Artificial Intelligence
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
Adaptive quasiconformal kernel discriminant analysis
Neurocomputing
Relevance learning in generative topographic mapping
Neurocomputing
A general framework for dimensionality-reducing data visualization mapping
Neural Computation
Directional discriminant analysis based on nearest feature line
ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part II
Discriminative dimensionality reduction mappings
IDA'12 Proceedings of the 11th international conference on Advances in Intelligent Data Analysis
An indication of unification for different clustering approaches
Pattern Recognition
Using nonlinear dimensionality reduction to visualize classifiers
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I
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In this paper, a kernelized version of clustering-based discriminant analysis is proposed that we name KCDA. The main idea is to first map the original data into another high-dimensional space, and then to perform clustering-based discriminant analysis in the feature space. Kernel fuzzy c-means algorithm is used to do clustering for each class. A group of tests on two UCI standard benchmarks have been carried out that prove our proposed method is very promising.