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.

Such elements provide a bridge to component analysis and the initial work of ontologies.

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