May, 2018


Complete and Simple PCA SVD Tutorial Note

Ref: PCA is the major method to reduce features/variables before you train your data in the machine learning. It uses the top K most variance transformed features to represent the original N features (assume N>>K). For example, we have food consumption of 17 types of food in grams per person per week for every country in the UK.   Maybe even after you view the above table for 5 minutes, you are hardly to get some patterns. But if you use PCA to extract theRead More