Statistical learning method reading notes (16)-principal component analysis

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Principal component analysis (PCA) is a commonly used unsupervised learning method. This method uses orthogonal transformation to convert the observation data represented by linear dependent variables into a few data represented by linear independent variables. Irrelevant variables are called principal components.

1. Overall principal component analysis

There may be correlations between the variables of the data, which increases the difficulty of analysis. Consider replacing related variables with a few irrelevant variables to represent data, and it is required to retain most of the information in the data.

The main purpose of principal component analysis is to reduce dimensionality, so generally choose $k(k<<m)$ principal components (linear independent variables) instead of$The\; m$ original variables (linearly dependent variables) simplify the problem and retain most of the information of the original variables.

2. Sample principal component analysis

Can be compared to Example 16.1.

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