Sentence examples for dimensional data are from inspiring English sources

Two dimensional data are collected by an array of 80 vertical position sensitive 3He detectors.

Very large high dimensional data are common nowadays and they impose new challenges to data-driven and data-intensive algorithms.

One of the most widely-used procedures for dimensionality reduction of high dimensional data is Principal Component Analysis (PCA).

Without appropriate modeling techniques, the high volume and high dimensional data is a burden rather than an advantage.

Clustering with fast algorithms large samples of high dimensional data is an important challenge in computational statistics.

When a large volume of (not very high dimensional) data is arriving continuously, it is impossible and sometimes unnecessary to store all the data in memory, in particular if we are interested to provide real time statistical analyses.

The lack of a proper medium to depict the three dimensional data is one of the reasons why the 3-D capture of paintings seems not to have matured yet.

However, because the global structure of high dimensional data is not necessarily linear, low dimensional linear principal components fail to capture this structure adequately (Fig. 6) [57].

Learning weight vectors from high dimensional data is a difficult problem.

A major problem with dealing with such high dimensional data is the lack of reliable approaches to investigate and compare patient-specific gene expression profiles separate to the construction of supervised models.

A standard analysis method in modeling high dimensional data is to fit the same statistical model individually to each variable (metabolite) and test for the contrast or effect of interest using the hypothesis testing framework.