Principal Component Analysis Explained Visually

“Principal component analysis (PCA) is a technique used to emphasize variation and bring out strong patterns in a dataset. It’s often used to make data easy to explore and visualize.”

PCA is very useful for pulling information out of large datasets, particularly multi-dimensional datasets, where correlations and relationships between different variables might otherwise have been difficult to see.

Principal Component Analysis Explained Visually

One thought on “Principal Component Analysis Explained Visually

  1. […] Principal Component Analysis is an example of a very low-level sort of machine learning algorithm. By identifying things that are¬†probably (within some statistical certainty) in a training dataset, you can identify the same things in a real-world dataset. This is why Google’s Deep Dream needs a training image — it needs to know what to look for. […]


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