Dimensionality Reduction: Principal Component Analysis (PCA)
Principal Component Analysis (PCA) belongs to which type of learning?
The main purpose of PCA is to:
PCA reduces data dimensions by:
The first principal component captures:
Principal components in PCA are:
One utility of PCA is that reduced features can be:
In PCA, eigenvectors represent:
Eigenvalues in PCA indicate:
Eigenvectors and eigenvalues are obtained from the:
Principal components are selected mainly based on: