[mlpack] GSoC query

[Manish Kumar]

I went through the paper you attached the link to. It mainly describes the
LMNN gradient based solver. But there I found something that can be a
little demotivating for SDP approach. Please tell me what do you think?

"The semidefinite program in the previous section grows in complexity with
the number of training examples(n), the number of target neighbors (k), and
the dimensionality of the input space (d).  In particular, the objective
function is optimized with respect to O(kn2) large margin constraints of
type(a)and(b), while the Mahalanobis distance metric itself is ad×dmatrix.
Thus, for even moderately large and/or high dimensional data sets, the
required optimization(though convex) cannot be solved by standard
off-the-shelf packages (Borchers, 1999)."

https://dl.acm.org/citation.cfm?id=1390302
Please see page 3, First Paragraph of Solver section.

Sorry, if I am disturbing you a lot.

On 16 March 2018 at 20:19, Ryan Curtin <ryan at ratml.org> wrote:

> On Fri, Mar 16, 2018 at 08:18:10PM +0530, Manish Kumar wrote:
> > Thanks Ryan for helping me out. I got little anxious by learning the fact
> > that LMNN+LRSDP may not work and was thinking that my GSoC project may go
> > futile.
> >
> > I do want to continue LMNN+LRSDP. I also see a good opportunity to do
> > something new while working on LMNN+LRSDP and that's why I never planned
> to
> > leave it but was looking for a perfect time for a project like this.
> >
> > I will stick to the proposal and will try to achieve the results.
> >
> > I just want to propose a little modification to the proposal. Tell me, if
> > that sounds fair.
> > I went through BoostMetric literature and find it as a significant
> > improvement over LMNN. So, can we at least include BoostMetric as a part
> of
> > the project apart from LMNN. I am sure that it will be a pretty good
> > addition and currently except author's implementation there is no other
> > implementation out there. Moreover, existing implementation takes the
> > exponential loss into consideration and we may include implementation
> based
> > on logistic loss function which shows improvement over the exponential
> one.
> > If that's okay, I will make some small changes to timeline and proposal.
> > This way we could have some pretty awesome metric learning algorithms.
>
> That sounds reasonable to me.  I think that BoostMetric is a
> generalization of LMNN, so maybe through some clever templatization you
> can support the other components.  I haven't read the paper in detail
> though so I am not fully sure.
>
> --
> Ryan Curtin    | "Do they hurt?"
> ryan at ratml.org |   - Jessica 6
>
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