The moment of inertia and the linear discriminant function
In this note, we show that the characteristic vector of the moment of inertia matrix associated with the first or last characteristic root corresponds to the best linear discriminant function in the situation where the data is a mixture of two multivariate normal distributions with proportional covariance matrices. This result may prove useful as a part of many outlier detection methods. We also describe a small simulation study which illustrates the computational efficiency of the new method.
Volume (Year): 71 (2005)
Issue (Month): 1 (January)
|Contact details of provider:|| Web page: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description|
|Order Information:|| Postal: http://www.elsevier.com/wps/find/supportfaq.cws_home/regional|
When requesting a correction, please mention this item's handle: RePEc:eee:stapro:v:71:y:2005:i:1:p:39-46. See general information about how to correct material in RePEc.
If references are entirely missing, you can add them using this form.