Saturday, December 31, 2016

PCA analysis explained (Python)

Here is a good tutorial showing how to reduce the number of dimensions / features using SVD and then PCA in Python.
First, we use SVD to obtain covariance matrix, and then we sort the eigenvectors in descending order. We use only top k eigenvectors to create a new projection space, where we transform data using only k dimensions.

Full tutorial:

https://plot.ly/ipython-notebooks/principal-component-analysis/#Projection-Matrix

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