IEEE Trans Image Process - Direct discriminant locality preserving projection with Hammerstein polynomial expansion.

Tópicos

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Resumo

Discriminant locality preserving projection (DLPP) is a linear approach that encodes discriminant information into the objective of locality preserving projection and improves its classification ability. To enhance the nonlinear description ability of DLPP, we can optimize the objective function of DLPP in reproducing kernel Hilbert space to form a kernel-based discriminant locality preserving projection (KDLPP). However, KDLPP suffers the following problems: 1) larger computational burden; 2) no explicit mapping functions in KDLPP, which results in more computational burden when projecting a new sample into the low-dimensional subspace; and 3) KDLPP cannot obtain optimal discriminant vectors, which exceedingly optimize the objective of DLPP. To overcome the weaknesses of KDLPP, in this paper, a direct discriminant locality preserving projection with Hammerstein polynomial expansion (HPDDLPP) is proposed. The proposed HPDDLPP directly implements the objective of DLPP in high-dimensional second-order Hammerstein polynomial space without matrix inverse, which extracts the optimal discriminant vectors for DLPP without larger computational burden. Compared with some other related classical methods, experimental results for face and palmprint recognition problems indicate the effectiveness of the proposed HPDDLPP.

Resumo Limpo

discrimin local preserv project dlpp linear approach encod discrimin inform object local preserv project improv classif abil enhanc nonlinear descript abil dlpp can optim object function dlpp reproduc kernel hilbert space form kernelbas discrimin local preserv project kdlpp howev kdlpp suffer follow problem larger comput burden explicit map function kdlpp result comput burden project new sampl lowdimension subspac kdlpp obtain optim discrimin vector exceed optim object dlpp overcom weak kdlpp paper direct discrimin local preserv project hammerstein polynomi expans hpddlpp propos propos hpddlpp direct implement object dlpp highdimension secondord hammerstein polynomi space without matrix invers extract optim discrimin vector dlpp without larger comput burden compar relat classic method experiment result face palmprint recognit problem indic effect propos hpddlpp

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