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An important characteristic of linear dimensionality reduction methods such as PCA is the intuitive interpretation of components in the low-dimensional space in terms of the original dimensions.
Inspection of a scree plot of unrotated components was used to determine the number of factors, followed by varimax rotation of the final solution to assist in the interpretation of components.
Principal component analysis (PCA) is a 100 year old mathematical technique credited to Karl Pearson [ 1] and its properties as well as the interpretation of components have been investigated extensively [ 2- 9].
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The questionnaire proposed several interpretations of components of the Swiss cheese model: a) slice of cheese, b) hole, c) arrow, d) active error, e) how to make the system safer.
To aid on interpretation of these components, several rotation techniques revealed the presence of a simple structure with three components.
Birth weight and arm circumference have high correlations (r > 0.5) with both components in the boys and girls analyses and this fits in with our interpretation of the components as they are measurements of both skeletal size and fat.
The interpretation of the components has been facilitated by the application of the varimax rotation to the component matrix.
For more detailed interpretation of these components, see Ref. 2. A notable limitation of the new expression for the local component, UEGLS, is that it lacks the simplicity of UEGL; its application requires a familiarity with the lead field concept.
To a large extent, the interpretation of principal components is generally guided by the degree to which each variable is associated with a particular component [ 47].
When considered in the framework of piezoelectricity, an energetic interpretation of these components is given.
However, the identification and interpretation of independent components is time-consuming and involves subjective decision making.
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