PCA
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(Created page with "== Introduction == Principal Components Analysis (PCA) is a dimensionality reduction algorithm that can be used to significantly speed up your unsupervised feature learning algor...") |
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the top (principal) eigenvector of <math>\textstyle \Sigma</math>, and <math>\textstyle u_2</math> is | the top (principal) eigenvector of <math>\textstyle \Sigma</math>, and <math>\textstyle u_2</math> is | ||
the second eigenvector.\footnote{For a mathematical derivation/formal justification | the second eigenvector.\footnote{For a mathematical derivation/formal justification | ||
| - | of this, see the CS229 lecture notes on PCA. http://cs229.stanford.edu/ | + | of this, see the CS229 lecture notes on PCA. <ref>http://cs229.stanford.edu</ref> You can use standard numerical linear algebra |
| - | software to find these (see | + | software to find these (see Implementation Notes). |
Concretely, let us compute the eigenvectors of <math>\textstyle \Sigma</math>, and stack | Concretely, let us compute the eigenvectors of <math>\textstyle \Sigma</math>, and stack | ||
the eigenvectors in columns to form the matrix <math>\textstyle U</math>: | the eigenvectors in columns to form the matrix <math>\textstyle U</math>: | ||
