Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Kernel independent component analysis
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Overview and recent advances in partial least squares
SLSFS'05 Proceedings of the 2005 international conference on Subspace, Latent Structure and Feature Selection
Edge detection in the feature space
Image and Vision Computing
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Several studies on classification of breast masses in mammograms have shown that shape features are highly successful in discriminating between malignant breast tumors and benign masses, as compared to edge-sharpness and texture features. However, the extraction of shape features requires accurate contours which are not easy to obtain automatically. In this paper, we propose to apply kernel principal component analysis (KPCA) to the problem of classification of breast masses, aiming to improve the discriminating power of each single feature in an expanded feature space derived from a centered kernel matrix. We also aim to improve the discriminating capability of different feature combinations in other transformed, more informative, lower-dimensional feature spaces, especially with the edge-sharpness and texture features. Fisher's linear discriminant analysis (FLDA) is employed to evaluate the classification capability of the transformed features via KPCA. The methods were tested with a set of 57 regions in mammograms, of which 20 are related to malignant tumors and 37 to benign masses, represented using one shape feature, three edge-sharpness features, and 14 texture features. The classification performance of the edge-sharpness and texture features, via KPCA transformation, was significantly improved from 0.75 to 0.85 in terms of the area under the receiver operating characteristics curve.