Examples of 'high-dimensional' in a sentence
Meaning of "high-dimensional"
high-dimensional (adjective) - This phrase is often used in mathematics or scientific contexts to describe spaces, data sets, or objects that have a large number of dimensions or variables
How to use "high-dimensional" in a sentence
Basic
Advanced
high-dimensional
Natural signals lie in a high-dimensional space.
High-dimensional statistics relies on the theory of random vectors.
Variable selection for high-dimensional data.
High-dimensional spaces frequently occur in mathematics and the sciences.
The end result is a high-dimensional state space.
We propose a variable selection procedure for clustering suited to high-dimensional contexts.
But in truly high-dimensional space it has a very strong utility.
They do not really scale to high-dimensional spaces.
High-dimensional data are typically handled as laying in a single subspace of the original space.
Images like these are very high-dimensional statistics.
The data is high-dimensional and produces numerical or symbolic information in the form of decisions.
Multivariate statistics in high-dimensional spaces.
Analysis of sparse high-dimensional data - measured and perceived quality of indoor air.
Actually represent symmetries of this high-dimensional object.
High-dimensional guides will not tell us how to live or make our day-today decisions.
See also
Adaptation of algorithms to high-dimensional data.
Equivalently, every high-dimensional bounded symmetric convex set has low-dimensional sections that are approximately ellipsoids.
The method can be also extended to high-dimensional images.
Characterize for which high-dimensional integration problems QMC is superior to MC.
These models can be projected into a high-dimensional space.
This is a high-dimensional math problem.
On universal oracle inequalities related to high-dimensional linear models.
High-dimensional statistical inference.
Semantic and associative priming in a high-dimensional semantic space.
In high-dimensional topology, characteristic classes are a basic invariant, and surgery theory is a key theory.
We describe in this manuscript a high-dimensional indexing technique called eCP.
These models are then usedfor discrimination and clustering of high-dimensional data.
How to navigate and interact in a high-dimensional space without incurring a lot of simulation.
He is also well known for fundamental contributions to high-dimensional knot theory.
They are based on high-dimensional data, such as images.
This can be particularly useful in settings with high-dimensional covariates.
In this thesis, we focus on high-dimensional predictors and a high-dimensional response.
Experiments were carried out using remote sensing high-dimensional image data.
It 's a memory of very high-dimensional patterns, like the things that come from your eyes.
His main research interests are in nonparametric and high-dimensional statistics.
Specific models for the treatment of high-dimensional data ( individuals characterized by a large number of features ).
A very important class of models are those where the data is high-dimensional.
Parallel coordinates are a common way of visualizing high-dimensional geometry and analyzing multivariate data.
In particular, he studies kernel methods for extracting regularities from possibly high-dimensional data.
Singularities of toric varieties give examples of high-dimensional singularities that are easy to resolve explicitly.
In addition to this, the method is validated on synthetic data and in high-dimensional settings.
Hierarchical axis indexing to work with high-dimensional data in a lower-dimensional data structure.
It provides a clustering and a visual low-dimensional representation of a set of high-dimensional observations.
Abstract, This document contributes to high-dimensional statistics for multivariate GARCH processes.
We investigate new methods to handle the similarity search problem in high-dimensional spaces.
The structures assembled formed enclosures for high-dimensional holes that the team have dubbed cavities.
However, maintaining the covariance matrix is not feasible computationally for high-dimensional systems.
The third chapter proposes a probabilistic high-dimensional mixture model on the noisy patches.
His research focuses on developing statistical methods for complex and high-dimensional data.
Feature extraction transforms the data in the high-dimensional space to a space of fewer dimensions.
