Spectroscopy at the leaf and canopy scales has attracted considerable interest in plant
ecology over the past decades. Using reflectance spectra, ecologists can infer plant traits …

When dealing with datasets comprising high‐dimensional points, it is usually advantageous
to discover some data structure. A fundamental information needed to this aim is the …

All physical laws are described as mathematical relationships between state variables.
These variables give a complete and non-redundant description of the relevant system …

Dealing with uncertainty in applications of machine learning to real-life data critically
depends on the knowledge of intrinsic dimensionality (ID). A number of methods have been …

Dimensionality reduction methods are preprocessing techniques used for co** with high
dimensionality. They have the aim of projecting the original data set of dimensionality N …

This paper is concerned with the estimation of a local measure of intrinsic dimensionality
(ID) recently proposed by Houle. The local model can be regarded as an extension of …

Researchers have long considered the analysis of similarity applications in terms of the
intrinsic dimensionality (ID) of the data. This theory paper is concerned with a generalization …

How many training data are needed to learn a supervised task? It is often observed that the
generalization error decreases as n− β where n is the number of training examples and β is …

This paper addresses the following questions pertaining to the intrinsic dimensionality of any
given image representation:(i) estimate its intrinsic dimensionality,(ii) develop a deep neural …

In the past decade the development of automatic intrinsic dimensionality estimators has
gained considerable attention due to its relevance in several application fields. However …