Adaptive quasiconformal kernel discriminant analysis
Neurocomputing
A Criterion for Learning the Data-Dependent Kernel for Classification
ADMA '07 Proceedings of the 3rd international conference on Advanced Data Mining and Applications
An MRF-based kernel method for nonlinear feature extraction
Image and Vision Computing
Establishing semantic relationship in inter-query learning for content-based image retrieval systems
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Representation of a fisher criterion function in a kernel feature space
IEEE Transactions on Neural Networks
Information Sciences: an International Journal
Proceedings of the 4th International Conference on Uniquitous Information Management and Communication
Kernel self-optimization learning for kernel-based feature extraction and recognition
Information Sciences: an International Journal
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A criterion is proposed to optimize the kernel parameters in kernel-based biased discriminant analysis (KBDA) for image retrieval. Kernel parameter optimization is performed by optimizing the kernel space such that the positive images are well clustered while the negative ones are pushed far away from the positives. The proposed criterion measures the goodness of a kernel space, and the optimal kernel parameter set is obtained by maximizing this criterion. Retrieval experiments on two benchmark image databases demonstrate the effectiveness of proposed criterion for KBDA to achieve the best possible performance at the cost of a small fractional computational overhead.