Simultaneous Variable Selection and Sensor Selection using Convex Penalties
by Ernest Fokoue.
This paper adapts results and tools from the traditional
D-optimality machinery to derive an efficient technique for
simultaneous variable selection and sensor selection.
The proposed approach provides the advantage that the underlying variable selection
problem is formulated as a convex optimization task that yields sparse
parameter estimates, thereby helping isolate relevant variables efficiently.
The theoretical derivation of the proposed method is presented,
along with a discussion of its unique benefits.
Variable Selection, Convex optimization, D-Optimality, Bayesian analysis,
Sensor selection, Sparsity
Ernest Fokoue, firstname.lastname@example.org
Khattree, Ravi, email@example.com
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