PCA and SPCA Tutorial

Tags

Algorithms, data miners, Data Mining, information retrieval, minerazzi, mining, news, PCA, statistics, SVD, text mining, tools

The PCA and SPCA Tutorial is back.

This is an improved and updated legacy tutorial on Principal Component Analysis (PCA) and one of its variants, Standardized PCA (SPCA). Both are techniques for identifying unknown trends in multidimensional data sets.

Despite the fact that one can find online many tutorials on PCA, undergraduate and graduate students are rarely exposed to the algorithm.

On the other hand, the fact that SVD can be used in both PCA and LSI (LSA) have induced some to assume, that PCA is LSI. Actually, PCA is neither LSI (LSA) nor Factor Analysis. Why? Good question. Check the tutorial at

http://www.minerazzi.com/tutorials/pca-spca-tutorial.pdf

 

Being Referenced by NVIDIA: CUDA DXT Compression

I was not aware that NVIDIA published early this year the white paper
High Quality DXT Compression using CUDA, in which in REF 6 of the paper they kindly reference one of my tutorials on eigenvalues and eigenvectors:

http://www.miislita.com/information-retrieval-tutorial/matrix-tutorial-3-eigenvalues-eigenvectors.html

Written by Ignacio Castano, the NVIDIA paper describes an image compression technique using, eigenpairs theory, the power method, and other standard numerical techniques. The way they’ve worked around some “technicals” is quite interesting. A great example of linear algebra in action.