An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Clustering Algorithms
Extraction of Shift Invariant Wavelet Features for Classification of Images with Different Sizes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture features for DCT-coded image retrieval and classification
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 06
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
Mercer kernel-based clustering in feature space
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Extended Gaussian kernel version of fuzzy c-means in the problem of data analyzing
Expert Systems with Applications: An International Journal
Fuzzy Optimization and Decision Making
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A Fuzzy C-Means algorithm with a Divergence-based Kernel (FCMDK) for clustering Gaussian Probability Density Function (GPDF) data is proposed in this paper. The proposed FCMDK is based on the Fuzzy C-Means algorithm and employs a kernel method for data transformation. The kernel method adopted in the proposed FCMDK is used to transform input data into a feature space of a higher dimensionality so that the nonlinear problems residing in input space can be linearly solved in the feature space. In order to deal with GPDF data, a divergence-based kernel employing a divergence distance measure for its similarly measure is used for data transformation. The proposed FCMDK is used for clustering GPDF data in an image classification model. Experiments and results on Caltech data sets demonstrate that the proposed FCMDK is more efficient than other conventional algorithms.