Learning a Gaussian basis for spectra representation aimed at reflectance classification
In this paper, we present a method which aims at learning a Gaussian basis which can be used to represent the reflectance spectra in the image while yielding a high recognition rate when used as input to an SVM classifier. To do this, we view the reflectance spectra as a Gaussian mixture and depart from a maximum-likelihood formulation which allows the introduction of posterior probabilities as a means to computing the mixture weights. This formulation permits the update of the Gaussian basis...[Show more]
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|Source:||Graph connectivity in sparse subspace clustering|
|01_Robles-Kelly_Learning_a_Gaussian_basis_for_2011.pdf||2.08 MB||Adobe PDF||Request a copy|
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