Principal Components Analysis
Free SOA Exam SRM (Statistics for Risk Modeling) lesson in Unsupervised Learning Techniques. 19 min read, ~2,913 words.
Principal components are linear combinations of the original predictors that capture maximum variance, each uncorrelated with earlier components. The first PC is the direction of greatest data variation; the second PC is the highest-variance direction orthogonal to it. Loadings are the coefficients (unit-norm eigenvectors of the covariance or correlation matrix)...
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What this lesson covers
- Content
- Example 1
- Example 2
- Example 3
- Example 4
- Example 5
- Common Mistakes
- Check Your Understanding
- Exam Shortcuts
Learning objectives
- 5a
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