Abstract
The nearest neighbor (NN) classifier is the most popular method for image-based object recognition. In NN classifier, the representational capacity of an image database and the recognition rate depend on how registered samples are selected to represent object’s possible variations and also how many samples are available. However, in practice, only a small number of samples are available for an object class. Hence linear representation methods are developed to generalize the representational capacity of available samples.
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He, R., Hu, B., Yuan, X., Wang, L. (2014). Correntropy and Linear Representation. In: Robust Recognition via Information Theoretic Learning. SpringerBriefs in Computer Science. Springer, Cham. https://doi.org/10.1007/978-3-319-07416-0_4
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