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Robust kernel-based learning for image-related problems

Robust kernel-based learning for image-related problems

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Robustness is one of the most critical issues in the appearance-based learning techniques. This study develops a novel robust kernel for kernel machines, and consequently improves their robustness in resisting noise for solving the image-related learning problems. By incorporating a robust ρ-function to reduce the influence of outlier components, this kernel gives more reasonable kernel values when images are seriously corrupted. The authors incorporate the proposed kernel into different kernel-based approaches, such as support vector machine (SVM) and kernel Fisher discriminant (KFD) analysis, to validate its performance on various visual learning problems of face recognition and data visualisation. Experimental results indicate that the proposed kernel can provide the superior robustness to the classical approaches.

Inspec keywords: support vector machines; data visualisation; face recognition; learning (artificial intelligence)

Other keywords: appearance-based learning; support vector machine; kernel machines; kernel Fisher discriminant analysis; robust kernel-based learning; face recognition; robust ρ-function; outlier components; visual learning problems; data visualisation; image-related learning problems; image-related problems

Subjects: Image recognition; Knowledge engineering techniques; Computer vision and image processing techniques; Graphics techniques

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