Analysis of a Complex of Statistical Variables into Principal Components¶
Hotelling, H. (1933). Analysis of a Complex of Statistical Variables into Principal Components. Journal of Educational Psychology, 417-441.
Cited by¶
2 citations across 2 artifacts.
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Primes¶
- Dimension
- finds the linear subspace preserving maximum variance; Hotelling's 1933 factor analysis
This sourceEigendecomposition-based formulation of principal-components analysis with a latent-variable (general-ability) interpretation.
- finds the linear subspace preserving maximum variance; Hotelling's 1933 factor analysis
- Dimensionality Reduction
- The deeper abstraction is that seeming complexity often decomposes into a small number of dominant patterns, and identifying those patterns reveals the underlying structure that high-dimensional representations obscure — a principle with applications from genomics to recommender systems to scientific visualization
This sourceEigendecomposition-based PCA and latent-variable interpretation.
- The deeper abstraction is that seeming complexity often decomposes into a small number of dominant patterns, and identifying those patterns reveals the underlying structure that high-dimensional representations obscure — a principle with applications from genomics to recommender systems to scientific visualization
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