Understanding Principal Component Analysis – Hacker Noon

The purpose of this post is to give the reader detailed understanding of Principal Component Analysis with the necessary mathematical proofs. We plot the data and find various patterns in it or use it to train some machine learning models. One way to think about dimensions is that suppose you have an data point x, if we consider this data point as a physical object then dimensions are merely a basis of view, like where is the data located when it is observed from horizontal axis or vertical axis. As the dimensions of data increases, the difficulty to visualize it and perform computations on it also increases. Variance: It is a measure of the variability or it simply measures how spread the data set is. Mathematically, it is the average squared deviation from the mean score.