Tech Notes

My notes on Statistics, Big Data, Cloud Computing, Cyber Security

Tag Archives: principal component analysis

Singular Value Decomposition (Also explains PCA)

The below is a reproduction of an answer in the Coursera discussion forum to the question that SVD was too complicated to understand and the material available on the web, directly goes into math instead of explaining what SVD and PCA really does.

Ive reproduced it here because it is too good and no part needs to be edited. Also once the course is archived this will not be available anymore.

Full credit goes to Pete Kazmier – https://class.coursera.org/dataanalysis-002/forum/profile?user_id=5233139

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After reading more and going back to the lectures, I think I finally understand the practical aspect of SVD/PCA when it comes to a data analysis. Most of the material I found online was focused on “how” these tools work and the math behind them, which is of little interest to me. I’m much more interested in the use of the tools. In short, I drive a car to work everyday, but I don’t care how its engine is built, only that it gets me from point A to point B. The following is my attempt to help others move past these lectures with some understanding of the material and how it relates to data analysis.

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Principal Component Analysis

It is a way of identifying patterns in data, and expressing the data in such a way as to highlight their similarities and differences. Since patterns in data can be hard to find in data of high dimension, where the luxury of graphical  representation is not available, PCA is a powerful tool for analysing data. The other main advantage of PCA is that once you have found these patterns in the data, and you compress the data, ie. by reducing the number of dimensions, without much loss of information. This technique used in image compression Read more of this post