AdvancedMultiple choice
You run PCA on raw features measured in wildly different units, and one feature dominates the first component. What happened?
- AThat feature genuinely carries most of the information
- BThe features were not standardised before the decomposition
- CThe covariance matrix failed to be positive semi-definite
- DToo many components were retained in the decomposition
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PCA, covariance matrices and what an eigenvalue is telling you
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