https://machine-learning-course.readthedocs.io/en/latest/content/unsupervised/pca.html

University of Michigan - Ann Arbor

This image does a good job at visualizing what PCA is accomplishing. The first principal component is determined such that it captures most of the variation from the data. The second principal component is then set in a direction that captures thee next most variation in the data. So in this example, we see that the first dimension is representing the data from the bottom left to top right of the graph. Then, the second dimension is placed to explain the variation of the data that isn’t explained by the first principal component.


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