In PCA, if an Eigenvalue is close to zero, it indicates that the corresponding Eigenvector may ________.
- be a principal component
- be discarded
- be of high magnitude
- explain high variance
If an Eigenvalue in PCA is close to zero, it means that the corresponding Eigenvector (principal direction) may "be discarded" as it explains very little variance within the data. This can help in reducing dimensionality while retaining essential information.
Loading...
Related Quiz
- How do conditional GANs (cGANs) differ from standard GANs?
- In a case where sparsity is important and you have highly correlated variables, which regularization technique might be most appropriate?
- Which of the following RNN variants uses both a forget gate and an input gate to regulate the flow of information?
- When both precision and recall are important for a problem, one might consider optimizing the ________ score.
- Which regression method assumes a linear relationship between the independent and dependent variables?