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Principal Component Analysis

author: Kostis G. date:

What are the principal components?

  • Dynamics with the largest variance.
  • Easy to see in low dimensional datasets, not so much when $d>2$.

Trying to generate an "Aha" moment.

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Trying to generate an "Aha" moment.

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Importance of components:
                          PC1     PC2
Standard deviation     1.4110 0.09563
Proportion of Variance 0.9954 0.00457
Cumulative Proportion  0.9954 1.00000

Result of PCA

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* The dataset after doing PCA.

Why do it?

  • Allows us to see the important directions/dynamics of the dataset.
  • Sometimes suggests variables to drop because of not important interactions with the rest of the set!

References

  • A tutorial on Principal Components Analysis, Jonathon Shlens
  • Computing and visualizing PCA in R, R-bloggers.
  • Principal Component Analysis, a how-to manual for R.