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 · We revisit the classical kernel method of approximation/interpolation theory in a very specific context motivated by the desire to obtain a robust procedure.

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 · 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 ...

In supervised manifold learning problems, data sets usually have a low intrinsic dimension, therefore, this geometric rate of increase can often be tolerated.

High-dimensional data refers to datasets with a large number of features or covariates, often exceeding the number of independent samples.

In this article, we provide a general overview of the different feature selection methods, their advantages, disadvantages, and use cases.

As we have seen in the preceding chapters a significant problem with Gaus- sian process prediction is that it typically scales as O(n3). For large problems.