  Tactical selection of experiments to estimate an underlying model is an
innate task across various fields. Since each experiment has costs associated
with it, selecting statistically significant experiments becomes necessary.
Classic linear experimental design deals with experiment selection so as to
minimize (functions of) variance in estimation of regression parameter.
Typically, standard algorithms for solving this problem assume that data
associated with each experiment is fully known. This isn't often true since
missing data is a common problem. For instance, remote sensors often miss data
due to poor connection. Hence experiment selection under such scenarios is a
widespread but challenging task. Though decoupling the tasks and using standard
data imputation methods like matrix completion followed by experiment selection
might seem a way forward, they perform sub-optimally since the tasks are
naturally interdependent. Standard design of experiments is an NP hard problem,
and the additional objective of imputing for missing data amplifies the
computational complexity. In this paper, we propose a maximum-entropy-principle
based framework that simultaneously addresses the problem of design of
experiments as well as the imputation of missing data. Our algorithm exploits
homotopy from a suitably chosen convex function to the non-convex cost
function; hence avoiding poor local minima. Further, our proposed framework is
flexible to incorporate additional application specific constraints.
Simulations on various datasets show improvement in the cost value by over 60%
in comparison to benchmark algorithms applied sequentially to the imputation
and experiment selection problems.
