PCA is popular method for dealing with the "curse of dimensionality".

It is a linear transform that provides the best projection that can represent a class; By using least square method onto its different features and associated data.

Features our teaching method, that will show of what happens to the data under various steps of PCA. In addition the different collection of statistics and information gained by PCA.

To ensure valid results, we will choose a popular data set and compare our results with the results of PCA on matlab or python sklearn.

To build an executable in a UNIX environment, open Terminal navigate to PCA directory and type: $ g++ -std=c++11 -o PCA main.cpp src/Matrix.cpp src/PCA.cpp

#Learning Sources Visit http://setosa.io/ev/principal-component-analysis/ for a graphical, simplified explanation of PCA.