Guide to principal component analysis

- 1 min


Principle component analysis (PCA) is a way to reduce dimensions of data. It was introduced by Karl Pearson in 1901 and used in diverse set of fields. In this post I’ll talk about use of PCA in context of machine learning. Algorithm use rather extensive use of linear algebra concepts which I will introduce along the post. To introduce the analogy with a simples example let’s take a look at the following dataset visualized.

Work in progress ,,,

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