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Cognitive Vision Systems (CVSys) are systems that combine computer vision with reasoning and semantic interpretation components to achieve high-level tasks.
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In 2013, an image interpretation component was added.
For interpretation, principal components were rotated using the varimax rotation method, an orthogonal rotation procedure.
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.
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].
According to this interpretation, these components should have been elicited contralateral to the cued movement goal location on the left or right side, but that was not what was observed.
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.
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].
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