Is there any different between feature selection and pca? If there is could anyone please kindly explain for me please? First of sorry for asking a possibly beginner question, but i don't understand pca seems to be the same as feature selection, but when i search online they seems to be talked differently. What people usually say is PCA for reducing dimensionality and feature selection for selecting feature. Didn't those two do the same thing? Reducing number of attributes/features/dimension? Please kindly help me understand. Thank you
PCA reduces dimensionality by generating orthogonal (i.e. uncorrelated) &quot;new&quot; features from the original features. This can be very useful, e.g. when you do linear regression. So principle components &quot;transform&quot; the original features to orthogonal ones. You can see this as some kind of feature engineering. Usually PCA will reduce the number of features which can be very helpful. See Ch. 10.2 in ISL.
Feature selection basically means to choose features which perform well in some model (e.g. have high explanatory power) or exclude features which are noisy. This can be any kind of features (transformed or original features). So feature selection is a very broad term and may be linked to various different methods.