A TUTORIAL ON PRINCIPAL COMPONENT ANALYSIS Derivation , Discussion and Singular Value Decomposition

@inproceedings{Shlens2003ATO,
  title={A TUTORIAL ON PRINCIPAL COMPONENT ANALYSIS Derivation , Discussion and Singular Value Decomposition},
  author={Jon Shlens},
  year={2003}
}
  • Jon Shlens
  • Published 2003
Principal component analysis (PCA) is a mainstay of modern data analysis a black box that is widely used but poorly understood. The goal of this paper is to dispel the magic behind this black box. This tutorial focuses on building a solid intuition for how and why principal component analysis works; furthermore, it crystallizes this knowledge by deriving from first principals, the mathematics behind PCA . This tutorial does not shy away from explaining the ideas informally, nor does it shy away… CONTINUE READING
Highly Cited
This paper has 72 citations. REVIEW CITATIONS

6 Figures & Tables

Topics

Statistics

051015'05'07'09'11'13'15'17
Citations per Year

72 Citations

Semantic Scholar estimates that this publication has 72 citations based on the available data.

See our FAQ for additional information.