Hi all,
I'm a graduate student at UIUC who is currently pursuing the research work
related to low-rank matrices recovery & Robust PCA. This kind of techniques
turned out to be very useful in applications in different areas (e.g.,
matrix completion for the Netflix-like recommendation systems, image
alignment, etc). In short, it can be seen as the matrix extension of the
l-1 minimization algorithms (such as Lasso) on vectors. If you think this
is a good component for sklearn, I'm very glad to work on it during this
summer via the GSoC 2012.
Here is a list of related references:
http://perception.csl.uiuc.edu/matrix-rank/home.html
Looking forward to hearing from you guys!
Sincerely,
Kerui Min
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Virtualization & Cloud Management Using Capacity Planning
Cloud computing makes use of virtualization - but cloud computing
also focuses on allowing computing to be delivered as a service.
http://www.accelacomm.com/jaw/sfnl/114/51521223/
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