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Extracting Manifold Information from Point Clouds
A kernel based method is proposed for the construction of signature (defining) functions of subsets of $\mathbb{R}^d$.
Dimension Independent Data Sets Approximation and Applications to Classification
We revisit the classical kernel method of approximation/interpolation theory in a very specific context motivated by the desire to obtain a robust procedure to approximate discrete data sets by (super)level sets of functions that are merely continuous at the data set arguments but are otherwise smooth.
Improving LBP and its variants using anisotropic diffusion
The main purpose of this paper is to propose a new preprocessing step in order to improve local feature descriptors and texture classification.
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