As is easy to see qda is far less robust than lda for

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Unformatted text preview: stat597e/notes2/lda.pdf) [6] ( Regularized linear discriminant analysis and its application in microarrays ( MATHEMATICAL OPERATIONS OF LDA ( Application in face recognition and in market ( QDA:[7] ( Bayes QDA ( LDA & QDA (http://www.uni- 2x4.pdf) LDA and QDA in Matlab - October 7, 2009 We have examined the theory behind Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA) above; how do we use these algorithms in practice? Matlab offers us a function called c a s f ( lsiy that allows us to perform LDA and QDA quickly and easily. In class, we were shown an example of using LDA and QDA on the 2_3 data that is used in the first assignment. The code below reproduces that example, slightly modified, and explains each step. > la 23 > od _; > [,sml]=picm('; >U ape rnopX) > sml =sml(,:) > ape ape:12; wikicour Stat841&pr intable= yes 11/74 10/09/2013 Stat841 - Wiki Cour se Notes First, we do principal component analysis (PCA) on the 2_3 data to reduce the dimensionality of the original data from 64 dimensions to 2. Doing this makes it much easier to visualize the results of the LDA and QDA algorithms. > po (ape1201,sml(:0,) ''; > lt sml(:0,) ape1202, .) > hl o; > od n > po (ape21401,sml(0:0,) '.) > lt sml(0:0,) ape21402, r'; Recall that in the 2_3 data, the first 200 elements are images of the number two handwritten and the last 200 elements are images of the number three handwritten. This code sets up a plot of the data such that the points that represent a 2 are blue, while the points that...
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