COMPUTER SCIENCE AND ENGINEERING
MACHINE LEARNING
Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]


True


False


Either A or B


None of the above

Detailed explanation1: The eigenvalues tells us the amount of variability in the direction of its corresponding eigenvector. Therefore, the eigenvector with the largest eigenvalue is the direction with most variability. We call this eigenvector the first principle component (or axis).
Detailed explanation2: 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.
Detailed explanation3: Principal component analysis creates variables that are linear combinations of the original variables. The new variables have the property that the variables are all orthogonal.
Detailed explanation4: (The eigenvector for the k t h largest eigenvalue corresponds to the k t h principal component direction .) The k t h principal component of, , has maximum variance d 1 2 / N, subject to being orthogonal to the earlier ones.