For large multi-dimensional datasets, CA allows a reduction in the dimensionality of the data so that efficient visualization that captures most of the variation can occur [ 39].
On the other hand, feature extraction techniques seek to reduce the dimensionality of the data by mapping the feature space onto a new lower-dimensional space.
Each data point comprises D different attributes, where D controls the dimensionality of the data.
It reduces the dimensionality of the data set and identifies a new meaningful underlying variable.
where is an identity matrix of size d by d and d is the dimensionality of the data.
First, robust biplots and factor analysis were performed to get an overview of elemental associations and reduce the dimensionality of the data set.
The process of correlation is described in equation 1 Watermarks also depend on the dimensionality of the data, or the transform domain.
For mobile applications such as signature verification and handwritten analysis, PCA is applied initially to reduce the dimensionality of the data, followed by similarity measure.
Keeping the number of cluster nodes, the size and the dimensionality of the data arrays arbitrary implies a considerable complication for indexing purposes.
As the dimensionality of the data is increasing day-by-day, difficulty in analyzing the data is also increasing with the same pace.
PCA technique was used to reduce the dimensionality of the data set while retaining the variability presented in a data set as much as possible.
