Howtosignalprocessing
- Which of the following is correct for principal component analysis PCA?
- Is Principal Component Analysis effective?
- How do I talk about my PCA results?
- How do you interpret PCA principal component analysis?
Which of the following is correct for principal component analysis PCA?
(12) [4 pts] Which of the following are true about principal components analysis (PCA)? A: The principal components are eigenvectors of the centered data matrix.
Is Principal Component Analysis effective?
PCA gives the best possible representation of a p-dimensional dataset in q dimensions (q<p) in the sense of maximizing variance in q dimensions. A disadvantage is, however, that the new variables that it defines are usually linear functions of all p original variables.
How do I talk about my PCA results?
For a PCA, you might begin with a paragraph on variance explained and the scree plot, followed by a paragraph on the loadings for PC1, then a paragraph for loadings on PC2, etc. These would then be followed by paragraphs on sample scores for each of the PCs, with one paragraph for each PC.
How do you interpret PCA principal component analysis?
Interpretation of the principal components is based on finding which variables are most strongly correlated with each component, i.e., which of these numbers are large in magnitude, the farthest from zero in either direction. Which numbers we consider to be large or small is of course is a subjective decision.
