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data depth methods #9
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It is the first time I hear about this, sounds quite interesting! I gave it a quick read so please correct me if I am doing something wrong. In the Did you think on something like this? library(ddalpha)
library(rgl)
# example 1
ds <- depth.space.Mahalanobis(as.matrix(iris[1:4]), c(50, 50, 50))
plot3d(ds, col = as.numeric( iris[[5]]) )
# example 2
perm <- sample(150)
ds2 <- depth.space.Mahalanobis(as.matrix(iris[perm, 1:4]), c(50, 50, 50))
plot3d(ds2, col = as.numeric( iris[[5]][perm] ))
# example 3
clusters <- kmeans(scale(iris[1:4]), 3)
c.ord <- order(clusters$cluster)
ds3 <- depth.space.Mahalanobis(as.matrix(iris[c.ord, 1:4]), as.vector(table(clusters$cluster)))
plot3d(ds3, col = as.numeric( iris[[5]][c.ord])) The first one is really cool, the second one not so much. One would have to supply a class vector as a parameter or some unsupervised classifier like kNN, as in the third example. What do you think @topepo ? |
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I think something like embed(data, "DataDepth", classes = cl, ...) where |
No problem on time. For Also, I'll send you an invite to a repo that I'll be making public soon in case you are interested in what I've been doing in regards to my previous requests. I have some of the depth parts worked out already but your interface you be better than my |
The |
I've used t-SNE a lot (back when I used to actually analyze data for a living) and like it. However, I'm constrained to using methods where the projection can be applied to new data sets (based on estimates from the old/training data). I didn't think to make a general |
t-SNE works by gradient descent and in theory one can hold the old points fixed and apply it to new points only but as far as I know no one implemented it. Here is a cool package for different SNE variants: https://github.com/jlmelville/sneer I think it is not on CRAN. |
You might consider adding some of Tukey's data depth methods. R has a few packages that you could wrap including
ddalpha
(see this paper gives a pretty good description of that).The text was updated successfully, but these errors were encountered: