On Wed, Aug 30, 2017 at 2:21 AM, Melissa Weber Mendonça < profamelissa@gmail.com> wrote:
In general, in unbalanced problems you are looking for a solution with more rows than columns (i.e. more data than variables, in the statistical sense). So the problem does not have a closed form solution, but needs to be solved via iterative methods. The balanced case is solved by performing one SVD, and the unbalanced problem needs roughly one SVD per iteration, and in ill conditioned problems this might mean a large number of SVD's.
Together with a colleague we have studied a new method for this (cited in the docs), and I had to test it against other previously developed methods, so I had to implement the scikit for my research purposes. From what I gather, in Psychometrics it might be useful to use the unbalanced problem if you are looking to find redundancies in the variables, for example.
- Melissa
Em ter, 29 de ago de 2017 às 11:02, Scott Sievert <sievert.scott@gmail.com> escreveu:
How is the unbalanced problem different, and what applications does it have? I've used the Procrustes transform for some low dimensional embedding problems, and I'm curious to hear.
Scott
On Aug 29, 2017, 8:33 AM -0500, Melissa Weber Mendonça < profamelissa@gmail.com>, wrote:
Hello everybody,
My name is Melissa, I'm a professor and researcher at the Federal University of Santa Catarina, in Brazil. I'm an applied mathematician working with Numerical Optimization and I've been working for some time with Procrustes Problems. So I realized there was no implementation of methods to solve the unbalanced Procrustes problem in python, and I decided to build a scikit. Here it is:
- https://github.com/melissawm/skprocrustes
http://scikits.scipy.org/scikit-procrustes
All are welcome to criticize and to send pull requests if you feel you have something to contribute, I'd appreciate it. I hope this is useful for someone.
Hi Melissa, welcome! For those of us not very familiar with Procrustes Problems, it may be good to point out the differences between scipy.linalg.orthogonal_procrustes, scipy.spatial.procrustes and your scikit. And perhaps it makes sense to cross-link to/from your scikit in the docstrings of those scipy functions?
Cheers, Ralf
Cheers!
Melissa
p.s. I'm not an expert in packaging and so it's highly possible that my code is not optimal, if you have any suggestions I'd appreciate as I'm trying to get better at this :)
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