Many of recent studies in the past few years have returned to using principal components analysis.

One of the main methods used is principal component analysis.

In particular, omitting principal component analysis made no significant difference.

The corresponding tool in statistics is called principal component analysis.

Other methods include measurement error models and a particular kind of principal component analysis.

A similar analysis can be done using principal components analysis, which in earlier research was a popular method.

The principal component analysis was used to reduce the high dimensionality of the feature space.

Traditional techniques like principal component analysis do not consider the intrinsic geometry of the data.

This property is the basis for principal components analysis and canonical correlation.

This decorrelation is related to principal components analysis for multivariate data.

It is a combination of Principal component analysis and the kernel trick.

This can be achieved by different means of feature selection and successive principal components analysis.

The procedure thus appears to be the counterpart of principal component analysis for categorical data.

Not to be confused with Kernel principal component analysis.

"Explained variance" is routinely used in principal component analysis.

The number of the parameters can be reduced based on types of principal component analysis.

These are then reduced to 10-15 dimensions by principal component analysis, giving the appearance information .

It has applications to the statistics of principal components analysis and the singular value decomposition.

This is also reflected by the effectiveness of dimension reduction methods such as principal component analysis in many situations.

Principal component analysis can be employed in a nonlinear way by means of the kernel trick.

His team also performed principal component analyses, which is good at analysing multivariate data with minimal loss of information.

It is conceptually similar to principal component analysis, but applies to categorical rather than continuous data.

Classical examples include principal components analysis and cluster analysis.

They can also be suggested by sampling in extensive molecular dynamics trajectories and principal component analysis.

In Stata, the pca command provides principal components analysis.