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 …

For many large-scale applications in data mining, machine learning, and multimedia,
fundamental operations such as similarity search, retrieval, classification, clustering, and …

Abstract Accurate estimation of Intrinsic Dimensionality (ID) is of crucial importance in many
data mining and machine learning tasks, including dimensionality reduction, outlier …

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 …

Distance-based expansion models of intrinsic dimensionality have had recent application in
the analysis of complexity of similarity applications, and in the design of efficient heuristics …

In this paper we propose a framework for modeling the intrinsic dimensionality of data sets.
The models can be viewed as generalizations of the expansion dimension, which was …

Representations learned via self-supervised learning (SSL) can be susceptible to
dimensional collapse, where the learned representation subspace is of extremely low …

Properties of data distributions can be assessed at both global and local scales. At a highly
localized scale, a fundamental measure is the local intrinsic dimensionality (LID), which …

Data mining methods for outlier detection are usually based on non-parametric density
estimates in various variations. Here we argue for the use of local intrinsic dimensionality as …