Reducing Information Redundancy with Principal Components Analysis
:-) Thank you to the University of Kansas, the Hexacoral project at http://www.kgs.ku.edu/Hexacoral/ for the use of the beautiful animated GIF which is a visualization of Principle Component Analysis . I copied it and I hope they don't mind. This animation shows how 12 clusters of data [the different colors] are reduced to 3 Principle Components. [the 3 orthogonal vectors (perpendicular to each other)] To see the animation, click on it to view it in a new window. In a world where so much information is available, we must learn to manage information overload. When we have several data feed on the same subject, chances are that much of the information overlaps i.e. is redundant. Overlap, meaning that mathematically speaking, if you could and did draw circles representing the true information content of the each data set, the circles would overlap. This is important when we are trying to use the data to model some aspect of the real world. For example, if we are trying to analyze a...