Principal Component Analysis - Part 2

   |  12 minutes  |  2480 words  |  

This is the second post of the series about Principal Component Analysis (PCA). Whilst the first post provided a theoretical background, this post will discuss the actual implementation of the PCA algorithm and its results when applied to some example data.

Theory Recap

In the first post we learnt that PCA looks for a vector basis that can express the analysed data in a better (less redundant) way, whilst retaining as much information from the original data as possible. The vectors that form this vector basis are called principal components.

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machine learning  dimensionality reduction  principal components analysis  pca 

Principal Component Analysis - Part 1

   |  13 minutes  |  2655 words  |  

This is the first of the two post series about Principal Component Analysis (PCA). This post lays down important knowledge bricks that are needed to understand the core principles of the PCA algorithm. The second post will discuss the actual implementation and its results by applying it to various data sets.

Motivation

The world is becoming more data driven than ever before. We collect large amounts of data from arbitrary sources. This is often because we don’t know which data best describes the systems we are trying to understand. The fear of missing out on capturing important features forces us to collect a lot of redundant data.

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machine learning  dimensionality reduction  principal components analysis  pca