Hinton reports that his models are effective feature extractors over high-dimensional, structured data.

Summary statistics may be used to increase the acceptance rate of ABC for high-dimensional data.

It is a simple algorithm that scales well to high-dimensional data.

Algorithms that operate on high-dimensional data tend to have a very high time complexity.

Many machine learning algorithms, for example, struggle with high-dimensional data.

This makes it infeasible for application to high-dimensional data.

Features must be extracted from (more often than not) noisy, high-dimensional data.

The "curse of dimensionality" is often used as a blanket excuse for not dealing with high-dimensional data.

According to , four problems need to be overcome for clustering in high-dimensional data:

The term is often used synonymous with general clustering in high-dimensional data.