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Learning an appropriate distance metric is a critical problem in pattern recognition. This paper addresses the problem in semi-supervised metric learning and proposes a new regularized semi-supervised metric learning (RSSML) method using local topology and triplet constraint. Our regularizer is designed and developed based on local topology, which is represented by local neighbors from the local smoothness,...
In semi-supervised classification boosting, a similarity measure is demanded in order to measure the distance between samples (both labeled and unlabeled). However, most of the existing methods employed a simple metric, such as Euclidian distance, which may not be able to truly reflect the actual similarity/distance. This paper presents a novel similarity learning method based on the geodesic distance...
The optical film thickness, expressed as the product of the refractive index and the absolute film thickness, is often considered as a single parameter in optical elastohydrodynamic lubrication (EHL) studies. A theoretical study of the effect of refractive index upon the interference intensity has been carried out, providing a method for the evaluation of refractive index irrespective of the absolute...
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