Principal Component Analysis for Machine Learning - Translucent


Analyzing large data sets comes with multiple challenges. One of the challenges is to get data in the right structure for the analysis. Without preprocessing the data, your algorithms might have difficult time converging and/or take a long time execute. One of the techniques that we used at TCinc is Principal Component Analysis (PCA). The official definition of PCA from Wikipediai is "Principal component analysis (PCA) is a statistical procedure that uses orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components."