Dear Ryan,
I am currently working on my proposal for the Fixes to MVU and low-rank semidefinite programs and have come up with the following ideas: 1.generate random simple dataset, and compare normal MVU with MVU +LRSDP on it. do visualization of the procedure and the result in 2d/3d. 2. write unit tests and substitution for the original code in mlpack's MVU implementation and check their correctness over processing of the above datasets. 3. base on the observation of the result of 1 and 2, create datasets that particularly points out the issue ... and check step by step on that sample 4.(or maybe datasets with a special property such that it should always converge by an implementation of MVU + LRSDP and check if the expected result is met ) do you think any of the above ideas worth a try? Thanks! Daniel Li ________________________________ From: LI Xuran Sent: 17 March 2018 17:47:09 To: [email protected] Subject: MVU Bug Fix GSOC Hello Ryan, I am Daniel Li, a second-year student studying Artificial in the University of Edinburgh. I write fluent c++ code and is interested in taking up the quest to fix bugs regarding MVU and semidefinite programming in mlpack. I've read about scalable semidefinite manifold learning and other articles and set up mlpack on my own computer. Could you give me some advice as for where to start my research on the project as I familiar myself with the code base? Also is it a good idea to implement the MVU with dual-tree algorithm to compare with the current version of MVU using LRSDP? Thanks! Daniel Li
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