REM: relational entropy-based measure of saliency
Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing
Spectral clustering of ROIs for object discovery
DAGM'11 Proceedings of the 33rd international conference on Pattern recognition
A novel ant-based clustering algorithm using Renyi entropy
Applied Soft Computing
Regularized discriminant entropy analysis
Pattern Recognition
Regional and Entropy component analysis based remote sensing images fusion
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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We introduce kernel entropy component analysis (kernel ECA) as a new method for data transformation and dimensionality reduction. Kernel ECA reveals structure relating to the Renyi entropy of the input space data set, estimated via a kernel matrix using Parzen windowing. This is achieved by projections onto a subset of entropy preserving kernel principal component analysis (kernel PCA) axes. This subset does not need, in general, to correspond to the top eigenvalues of the kernel matrix, in contrast to the dimensionality reduction using kernel PCA. We show that kernel ECA may produce strikingly different transformed data sets compared to kernel PCA, with a distinct angle-based structure. A new spectral clustering algorithm utilizing this structure is developed with positive results. Furthermore, kernel ECA is shown to be an useful alternative for pattern denoising.