Add to ORKGMercer kernels are used for a wide range of image and signal processing tasks like de-noising, clustering, discriminant analysis etc. These algorithms construct their solutions in terms of the expansions in a high-dimensional feature space F. However, many applications like kernel PCA (principal component analysis) can be used more effectively if a pre-image of the projection in the feature space is available. In this paper, we propose a novel method to reconstruct a unique approximate pre-image of a feature vector and apply it for statistical shape analysis. We provide some experimental results to demonstrate the advantages of kernel PCA over linear PCA for shape learning, which include, but are not limited to, ability to learn and disting...
We consider the problem of reconstructing patterns from a feature map. Learning algorithms using ker...
©2006 SPIE--The International Society for Optical Engineering. One print or electronic copy may be m...
We consider the problem of reconstructing patterns from a feature map. Learning algorithms using ...
©2006 SPIE--The International Society for Optical Engineering. One print or electronic copy may be m...
Principal component analysis (PCA) is a popular tool for linear dimensionality reduc-tion and featur...
Segmentation involves separating an object from the background. In this work, we propose a novel seg...
Algorithms based on Mercer kernels construct their solutions in terms of expansions in a high-dimens...
Segmentation involves separating an object from the background. In this work, we propose a novel seg...
©2006 Springer. The original publication is available at www.springerlink.com: http://dx.doi.org/10....
Kernel PCA as a nonlinear feature extractor has proven powerful as a preprocessing step for classifi...
Kernel PCA as a nonlinear feature extractor has proven powerful as a preprocessing step for classifi...
Kernel PCA as a nonlinear feature extractor has proven powerful as a preprocessing step for classifi...
fbs mika smola raetsch klausgrst gmd de Algorithms based on Mercer kernels construct their solut...
Kernel Principal Component Analysis (Kernel PCA) is a useful technique to extract nonlinear structur...
We consider the problem of reconstructing patterns from a feature map. Learning algorithms using ker...
We consider the problem of reconstructing patterns from a feature map. Learning algorithms using ker...
©2006 SPIE--The International Society for Optical Engineering. One print or electronic copy may be m...
We consider the problem of reconstructing patterns from a feature map. Learning algorithms using ...
©2006 SPIE--The International Society for Optical Engineering. One print or electronic copy may be m...
Principal component analysis (PCA) is a popular tool for linear dimensionality reduc-tion and featur...
Segmentation involves separating an object from the background. In this work, we propose a novel seg...
Algorithms based on Mercer kernels construct their solutions in terms of expansions in a high-dimens...
Segmentation involves separating an object from the background. In this work, we propose a novel seg...
©2006 Springer. The original publication is available at www.springerlink.com: http://dx.doi.org/10....
Kernel PCA as a nonlinear feature extractor has proven powerful as a preprocessing step for classifi...
Kernel PCA as a nonlinear feature extractor has proven powerful as a preprocessing step for classifi...
Kernel PCA as a nonlinear feature extractor has proven powerful as a preprocessing step for classifi...
fbs mika smola raetsch klausgrst gmd de Algorithms based on Mercer kernels construct their solut...
Kernel Principal Component Analysis (Kernel PCA) is a useful technique to extract nonlinear structur...
We consider the problem of reconstructing patterns from a feature map. Learning algorithms using ker...
We consider the problem of reconstructing patterns from a feature map. Learning algorithms using ker...
©2006 SPIE--The International Society for Optical Engineering. One print or electronic copy may be m...
We consider the problem of reconstructing patterns from a feature map. Learning algorithms using ...