Jan 3, 2024 · The method is applied to both low dimensional examples and a standard high dimensioal benchmark problem (MNIST digit classification).

Aug 29, 2022 · Title:Dimension Independent Data Sets Approximation and Applications to Classification. Authors:Patrick Guidotti. View a PDF of the paper ...

Special functions, called data signals, are defined for any given data set and are used to succesfully solve supervised classification problems in a robust way ...

We revisit the classical kernel method of approximation/interpolation theory in a very specific context from the particular point of view of partial ...

Dimension Independent Data Sets Approximation and Applications to Classification. Patrick Guidotti University of California, Irvine Department of Mathematics

Dimension independent data sets approximation and applications to classification · P. Guidotti · Published in Advanced Modeling and… 29 August 2022 · Computer ...

We revisit the classical kernel method of approximation/interpolation theory in a very specific context from the particular point of view of partial ...

Our theory leverages the low-dimensional geometric structures in data sets and justifies the practical power of generative networks. We require no smoothness ...

Abstract. There are many scientific and engineering applications where an automatic detection of shape dimension from sam- ple data is necessary.

tion 8.4 we describe various methods for approximating the GP classification ... For the SD method a subset of the training data of size m was selected at.