Dimensional Reduction and Principal Component Analysis -- I

Normally when we are applying any of the machine learning concepts, we need to deal with a lot of matrices. Each matrix may have a lot of features or dimensions and then we will need to do a lot of computation. It may be prohibitive to run all the computations in a production environment, not counting the added problem of overfitting. In many occasions, it is also very useful to visualize the data. Due to our limitations as human beings, we are not able to visualize higher dimensions.