As the first step in the decommissioning of sasCommunity.org the site has been converted to read-only mode.

Here are some tips for How to share your SAS knowledge with your professional network.

# Difference between revisions of "Tips:Implement Regularized Discriminant Analysis in SAS"

Paulkaefer (Talk | contribs) m (gardening) |
|||

(4 intermediate revisions by 4 users not shown) | |||

Line 1: | Line 1: | ||

Friedman proposed Regularized Discriminant Analysis (RDA) to overcome multicollinearity and other causes that make LDA/QDA ill-conditioned. The core idea is to regularize illy-conditioned within class covariance matrix with the pooled covariance matrix for QDA or to regularize illy-conditioned pooled covariance matrix with a diagonal matrix for LDA. For details about this algorithm, check the book: Elements of Statistical Learning, Chapter 4, section 3.1. | Friedman proposed Regularized Discriminant Analysis (RDA) to overcome multicollinearity and other causes that make LDA/QDA ill-conditioned. The core idea is to regularize illy-conditioned within class covariance matrix with the pooled covariance matrix for QDA or to regularize illy-conditioned pooled covariance matrix with a diagonal matrix for LDA. For details about this algorithm, check the book: Elements of Statistical Learning, Chapter 4, section 3.1. | ||

− | To implement RDA, we output sufficient statistics using OUTSTAT= in PROC DISCRIM and make | + | To implement RDA, we output sufficient statistics using OUTSTAT= in PROC DISCRIM and make appropriate changes to relevant statistics, then use the scoring functionality for PROC DISCRIM to re-score the data with regularized covariance matrix. See link below for sample code on Regularized LDA. |

<!-- Insert any appropriate category tags. Note that it is important to put the categorys inside the <noinclude> block so the categories are only applied to the tip page. | <!-- Insert any appropriate category tags. Note that it is important to put the categorys inside the <noinclude> block so the categories are only applied to the tip page. | ||

Line 7: | Line 7: | ||

--> | --> | ||

<noinclude> | <noinclude> | ||

− | [[Category:Data Mining | + | [[Category:Data Mining]] |

+ | [[Category:DISCRIM Procedure]] | ||

</noinclude> | </noinclude> | ||

− | |||

− | |||

− | |||

− | |||

− | |||

− | |||

{{ExternalReadMore|http://www.sas-programming.com/2011/04/regularized-discriminant-analysis.html}} | {{ExternalReadMore|http://www.sas-programming.com/2011/04/regularized-discriminant-analysis.html}} | ||

− | |||

<!-- Please do not edit below this line, EXCEPT when promoting a tip --> | <!-- Please do not edit below this line, EXCEPT when promoting a tip --> | ||

<div style="float:right">Submitted By [[User:http://sas-programming.blospot.com|Liang Xie]]</div> | <div style="float:right">Submitted By [[User:http://sas-programming.blospot.com|Liang Xie]]</div> | ||

+ | |||

<noinclude> | <noinclude> | ||

− | [[Category:Tip | + | [[Category:Tip in Use]] |

</noinclude> | </noinclude> |

## Latest revision as of 17:40, 27 January 2017

Friedman proposed Regularized Discriminant Analysis (RDA) to overcome multicollinearity and other causes that make LDA/QDA ill-conditioned. The core idea is to regularize illy-conditioned within class covariance matrix with the pooled covariance matrix for QDA or to regularize illy-conditioned pooled covariance matrix with a diagonal matrix for LDA. For details about this algorithm, check the book: Elements of Statistical Learning, Chapter 4, section 3.1.

To implement RDA, we output sufficient statistics using OUTSTAT= in PROC DISCRIM and make appropriate changes to relevant statistics, then use the scoring functionality for PROC DISCRIM to re-score the data with regularized covariance matrix. See link below for sample code on Regularized LDA.

....see also