IEEE Trans Image Process - Design of non-linear kernel dictionaries for object recognition.

Tópicos

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Resumo

In this paper, we present dictionary learning methods for sparse signal representations in a high dimensional feature space. Using the kernel method, we describe how the well known dictionary learning approaches, such as the method of optimal directions and KSVD, can be made nonlinear. We analyze their kernel constructions and demonstrate their effectiveness through several experiments on classification problems. It is shown that nonlinear dictionary learning approaches can provide significantly better performance compared with their linear counterparts and kernel principal component analysis, especially when the data is corrupted by different types of degradations.

Resumo Limpo

paper present dictionari learn method spars signal represent high dimension featur space use kernel method describ well known dictionari learn approach method optim direct ksvd can made nonlinear analyz kernel construct demonstr effect sever experi classif problem shown nonlinear dictionari learn approach can provid signific better perform compar linear counterpart kernel princip compon analysi especi data corrupt differ type degrad

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