[mlpack] GSoC query

[Manish Kumar]

Hello,
I am Manish Kumar (IRC: manish7294). After yesterday's discussion on IRC, I
went looking for the other optimal reliable options related to metric
learning. And have picked out some relatively comparable alternatives to
LMNN by going through literature in detail.

1. BoostMetric
<https://pdfs.semanticscholar.org/65af/3d9b9424cebb1054aac6f71bf2e39a3b1994.pdf>(It
belongs to the category of supervised learning. It takes LMNN background as
its basis and tries to improve it by incorporating alternative exponential
loss function and a different optimization technique. Overall it combines
the characteristics of boosting and metric learning and has claimed to
outperform LMNN. See page 7 of literature for the results.)

2. ITML <http://www.cs.utexas.edu/users/pjain/pubs/metriclearning_icml.pdf>
(This one belongs to unsupervised-category and requires the external
knowledge of similar and dissimilar data points which acts as constraints.
Though constraints can be generated on the basis of labels, subsequently
shifting ITML to the supervised category. This one has shown results
comparable to LMNN as well.)

After discussions, I realized that it will not be a good idea to propose
something that doesn't ensure to work at the end. So, for the time being, I
have decided to put LMNN with LRSDP at the halt and continue it from the
same point in near future as a commendable test project.

At this point, I may need to re-design my proposal. So, I humbly request
you to give your feedback on my thought. I intend to include the
implementation of these two state-of-art algorithms, if favorable. I expect
them to give a solid boost to metric learning algorithms.


Thank you
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