IEEE Trans Image Process - Kernel maximum autocorrelation factor and minimum noise fraction transformations.

Tópicos

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Resumo

This paper introduces kernel versions of maximum autocorrelation factor (MAF) analysis and minimum noise fraction (MNF) analysis. The kernel versions are based upon a dual formulation also termed Q-mode analysis in which the data enter into the analysis via inner products in the Gram matrix only. In the kernel version, the inner products of the original data are replaced by inner products between nonlinear mappings into higher dimensional feature space. Via kernel substitution also known as the kernel trick these inner products between the mappings are in turn replaced by a kernel function and all quantities needed in the analysis are expressed in terms of this kernel function. This means that we need not know the nonlinear mappings explicitly. Kernel principal component analysis (PCA), kernel MAF, and kernel MNF analyses handle nonlinearities by implicitly transforming data into high (even infinite) dimensional feature space via the kernel function and then performing a linear analysis in that space. Three examples show the very successful application of kernel MAF/MNF analysis to: 1) change detection in DLR 3K camera data recorded 0.7 s apart over a busy motorway, 2) change detection in hyperspectral HyMap scanner data covering a small agricultural area, and 3) maize kernel inspection. In the cases shown, the kernel MAF/MNF transformation performs better than its linear counterpart as well as linear and kernel PCA. The leading kernel MAF/MNF variates seem to possess the ability to adapt to even abruptly varying multi and hypervariate backgrounds and focus on extreme observations.

Resumo Limpo

paper introduc kernel version maximum autocorrel factor maf analysi minimum nois fraction mnf analysi kernel version base upon dual formul also term qmode analysi data enter analysi via inner product gram matrix kernel version inner product origin data replac inner product nonlinear map higher dimension featur space via kernel substitut also known kernel trick inner product map turn replac kernel function quantiti need analysi express term kernel function mean need know nonlinear map explicit kernel princip compon analysi pca kernel maf kernel mnf analys handl nonlinear implicit transform data high even infinit dimension featur space via kernel function perform linear analysi space three exampl show success applic kernel mafmnf analysi chang detect dlr k camera data record s apart busi motorway chang detect hyperspectr hymap scanner data cover small agricultur area maiz kernel inspect case shown kernel mafmnf transform perform better linear counterpart well linear kernel pca lead kernel mafmnf variat seem possess abil adapt even abrupt vari multi hypervari background focus extrem observ

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