This paper presents a classifier named kernel-based nonlinear representor (KNR) for optimal representation of pattern features. Adopting the Gaussian kernel, with the kernel width adaptively estimated by a simple tech...This paper presents a classifier named kernel-based nonlinear representor (KNR) for optimal representation of pattern features. Adopting the Gaussian kernel, with the kernel width adaptively estimated by a simple technique, it is applied to eigenface classification. Experimental results on the ORL face database show that it improves performance by around 6 points, in classification rate, over the Euclidean distance classifier.展开更多
Previously, a novel classifier called Kernel-based Nonlinear Discriminator (KND) was proposed to discriminate a pattern class from other classes by minimizing mean effect of the latter. To consider the effect of the t...Previously, a novel classifier called Kernel-based Nonlinear Discriminator (KND) was proposed to discriminate a pattern class from other classes by minimizing mean effect of the latter. To consider the effect of the target class, this paper introduces an oblique projection algorithm to determine the coefficients of a KND so that it is extended to a new version called extended KND (eKND). In eKND construction, the desired output vector of the target class is obliquely projected onto the relevant subspace along the subspace related to other classes. In addition, a simple technique is proposed to calculate the associated oblique projection operator. Experimental results on handwritten digit recognition show that the algorithm performes better than a KND classifier and some other commonly used classifiers.展开更多
文摘This paper presents a classifier named kernel-based nonlinear representor (KNR) for optimal representation of pattern features. Adopting the Gaussian kernel, with the kernel width adaptively estimated by a simple technique, it is applied to eigenface classification. Experimental results on the ORL face database show that it improves performance by around 6 points, in classification rate, over the Euclidean distance classifier.
基金Supported by the key project of Chinese Ministry of Education(No.1051150)
文摘Previously, a novel classifier called Kernel-based Nonlinear Discriminator (KND) was proposed to discriminate a pattern class from other classes by minimizing mean effect of the latter. To consider the effect of the target class, this paper introduces an oblique projection algorithm to determine the coefficients of a KND so that it is extended to a new version called extended KND (eKND). In eKND construction, the desired output vector of the target class is obliquely projected onto the relevant subspace along the subspace related to other classes. In addition, a simple technique is proposed to calculate the associated oblique projection operator. Experimental results on handwritten digit recognition show that the algorithm performes better than a KND classifier and some other commonly used classifiers.
