Sentence examples for multidimensional data dimensionality from inspiring English sources

To analyze such multidimensional data, dimensionality reduction mapping techniques are used [27], mainly Principal Component Analysis (PCA) [28] and/or Multidimensional Scaling (MDS) [29].

Self-organizing maps are another way to reduce dimensionality by mapping multidimensional data to two dimensional space.

It retains maximum variance of multidimensional data whilst reducing their dimensionality.

Due to the high dimensionality of CUPrefs data, dimensionality reduction techniques were applied: Multidimensional scaling (MDS), correspondence analysis (CA), and cluster analyses.

The multidimensional data can define a multidimensional space defined by a Cartesian product of the dimensions.

The above framework [59] offers a unified way of understanding many dimensionality reduction techniques such as singular value decomposition (SVD), principal component analysis (PCA), non-negative matrix factorization (NMF), and others needed for multivariate analysis of various multidimensional data.

Two limitations about data dimensionality have to be relaxed.

Firstly, PCA is used to reduce data dimensionality.

Apply principal component analysis (PCA) to reduce the data dimensionality.

Let a set of multidimensional data points (vectors) be given.

This method reduces the data dimensionality by performing a covariance analysis between factors.