  A useful strategy to deal with complex classification scenarios is the
"divide and conquer" approach. The mixture of experts (MOE) technique makes use
of this strategy by joinly training a set of classifiers, or experts, that are
specialized in different regions of the input space. A global model, or gate
function, complements the experts by learning a function that weights their
relevance in different parts of the input space. Local feature selection
appears as an attractive alternative to improve the specialization of experts
and gate function, particularly, for the case of high dimensional data. Our
main intuition is that particular subsets of dimensions, or subspaces, are
usually more appropriate to classify instances located in different regions of
the input space. Accordingly, this work contributes with a regularized variant
of MoE that incorporates an embedded process for local feature selection using
$L1$ regularization, with a simultaneous expert selection. The experiments are
still pending.
