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About Tutorial
This document discusses various methods for determining the optimal number of components in principal component analysis (PCA). It examines the scree plot method, which looks for an "elbow" in a plot of eigenvalues against component numbers. For the crime dataset analyzed, there are two possible elbows, with 2 or 4 components selected. Looking at the cumulative variance explained favors 4 components. The Kaiser-Guttman rule and its variants also select 4 components for this dataset. Overall, the document analyzes different approaches for selecting the best number of PCA components for dimensionality reduction.
Detailed Information
| Author: | ['Ricardo Xixa'] |
|---|---|
| Publication Year: | 2014 |
| Pages: | 19 |
| Language: | English |
| Format: | |
| Price: | FREE |
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