Hi everyone. i was thinking about eigenspokes problem. Actually briefly looked thru one paper about it.
We basically said cluster detection doesn't work well on them. But it would seem to me that's just a matter of geometrical convenience. if we convert U stuff into hyperspherical vectors (and exclude the second norm from it), shouldn't that representation actually have very nice centroids? Or i am missing something fundamental here? But if that solves the problem, then it looks like we could have a preprocessor for clustering algorithms converting SVD output into hyperspherical vectors. so this basically would allow to run clustering after dimensionality reduction (and there's another reason why i wanted to do that but that's another discussion's subject). Thanks. -Dmitriy
